Alpha power eeg

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EEG alpha power and creative ideation

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Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4020761/

EEG Alpha Power Is Modulated by Attentional Changes during Cognitive Tasks and Virtual Reality Immersion

Variations in alpha rhythm have a significant role in perception and attention. Recently, alpha decrease has been associated with externally directed attention, especially in the visual domain, whereas alpha increase has been related to internal processing such as mental arithmetic. However, the role of alpha oscillations and how the different components of a task (processing of external stimuli, internal manipulation/representation, and task demand) interact to affect alpha power are still unclear. Here, we investigate how alpha power is differently modulated by attentional tasks depending both on task difficulty (less/more demanding task) and direction of attention (internal/external). To this aim, we designed two experiments that differently manipulated these aspects. Experiment 1, outside Virtual Reality (VR), involved two tasks both requiring internal and external attentional components (intake of visual items for their internal manipulation) but with different internal task demands (arithmetic vs. reading). Experiment 2 took advantage of the VR (mimicking an aircraft cabin interior) to manipulate attention direction: it included a condition of VR immersion only, characterized by visual external attention, and a condition of a purely mental arithmetic task during VR immersion, requiring neglect of sensory stimuli. Results show that: (1) In line with previous studies, visual external attention caused a significant alpha decrease, especially in parieto-occipital regions; (2) Alpha decrease was significantly larger during the more demanding arithmetic task, when the task was driven by external visual stimuli; (3) Alpha dramatically increased during the purely mental task in VR immersion, whereby the external stimuli had no relation with the task. Our results suggest that alpha power is crucial to isolate a subject from the environment, and move attention from external to internal cues. Moreover, they emphasize that the emerging use of VR associated with EEG may have important implications to study brain rhythms and support the design of artificial systems.

1. Introduction

The preeminent oscillatory phenomenon in brain neurodynamics is represented by the alpha rhythm (approximately 8–12 Hz), which is the dominant frequency in the human scalp EEG [1]. It is well known that EEG activity in the alpha band exhibits a significant change in a variety of conditions; depending on the kind of stimulus or task demand, a brain region responds either with a decrease in alpha power (Event-Related Desynchronization, ERD) or an alpha power increase (Event-Related Synchronization, ERS) [2, 3]. More particularly, a large body of the literature suggests that regions activated during a task exhibit ERD, whereas ERS occurs in regions irrelevant for the task, or regions which process distractors or potentially interfering cues [4–7].

Furthermore, recent studies propose an interpretation of human alpha rhythm in terms of a distinction between internally and externally directed attention.

For what concerns external attention, it is well known that alpha power decreases over occipital sites during visual stimulation [8] and over sensorimotor areas during sensorimotor tasks or movements [3]. Various studies relate the level of visual attention to the strength of oscillatory α activity, observing that greater external attention causes a decrease in alpha power, or a shift in the alpha rhythm toward the attended locations [9–11]. Alpha desynchronization has been associated to tasks requiring processing of relevant information in a variety of cognitive domains, but especially associated with visual perception [12–14]. It was thus hypothesized that the suppression of alpha activity is related to the strength of attention to external objects or stimuli required by the task [15].

Different results, however, have been recently observed in the auditory domain, where an increase of alpha has been linked to increased effort and/or processing [16–19]. Hence, the role of a task on the alpha-band power, even during processing of external inputs is still strongly debated, with possible significant differences in auditory and visual domains and in different tasks.

Conversely, a-band oscillations have been observed to increase during internal tasks, such as visual imagery or arithmetic operations [20–23]. An old influential hypothesis by Ray and Cole [24] assumes that alpha power increases during rejection tasks (internally directed attention), to reflect inhibition or rejection of incoming sensory information. Since an inward shift of attention is accompanied by an increase in alpha power, some authors suggested that ERS may be working to inhibit sensory processing and suppress distractors or potentially interfering cues [4–7, 25, 26] or more generally to implement a general inhibitory mechanism in the brain [26, 27].

Consequent to the previous observations is the idea that α-power modulation is strictly related with working memory (WM). During WM tests, selective attention may be operating to enhance the efficient use of limited memory resources, by enabling the encoding of relevant information and avoiding that memory capacity is degraded by interfering cues [28]. Indeed, several studies have shown a significant α-synchronization associated with memory load in experiments in which participants were presented items to be remembered for a short period [25, 29, 30]. An influential hypothesis is that alpha oscillations work as a filter mechanism able to inhibit an increasing number of distractors via a progressive α-power increase [31]. However, divergent results have been reported on this point. While Sauseng et al. [32] found that alpha activity increases with the number of distractors, other failed to report these changes [33, 34].

From the previous literature, we can conclude that while the relationship between alpha activity and attention mechanisms is well documented today, both when attention is directed toward external stimuli (ERD) and remembered items (ERS), the exact role played by alpha oscillations and its modulation by the task is still unclear. In particular, the alpha-inhibition hypothesis and the role of alpha activity during internal memory tasks continue to be questioned (for a recent review, see [35]). At least three important elements are involved in these procedures: maintenance of internal memory, processing of external stimuli, and task load requirements. As pointed out by van Moorselaar et al. [36], it is still unclear whether these aspects cooperate or are in conflict, and how they interact at the frontal and occipital level to ensure the better behavioural performance. Does alpha activity reflect an internal cognitive process, under the influence of top-down mechanisms which work to focus attention on the essential items, i.e., a shift between “bottom-up” and “top-down” requirements, as suggested by von Stein et al. [37, 38]? Or does it simply reflect the disengagement of attention from external stimuli? Does alpha desynchronization signal a major role of external sensory representation, whereas alpha synchronization emphasizes a major role of internal mental processing?

In order to examine these aspects, we need experiments which manipulate both external stimulation, cognitive processing requirements (i.e., task difficulty), and direction of attention (external vs. internal). In particular, we wish to investigate in which terms alpha power can be reduced by tasks which require an attentional focus to external items, how this desynchronization is affected by the task demand, and how it is affected by strong external stimulation in absence of specific tasks and finally by a mental process which requires isolation from the environment.

To reach our objective, the study comprises two subsequent but strictly related experiments: (i) changes in alpha-band power (i.e., ERD or ERS) were measured in laboratory, using a 13-electrode system, during two tasks which differently recruited visual and cognitive mechanisms (the first is a reading numbers task, the second a visual + arithmetic operation task). The results are used to assess ERD during attentive tasks that require external attention, and its modulation by the level of attention/involvement required. (ii) Changes in alpha-band power were quantified when participants interact with a business aircraft cabin in a Virtual Reality (VR) setting, to mimic conditions experienced by a passenger during an airplane travel. In this case, the EEG was obtained while the participant was immersed in a VR environment, conceived to simulate the main visual and acoustic characteristics of a cabin interior, ad hoc designed during the project. We assume that this condition strongly solicits the external visual/acoustic attention, even in the absence of a specific task. Finally, in the same condition (VR immersion), we asked the participants to perform a mental arithmetic task (internal attention) and to investigate the conflict between the external virtual immersion and the internal focus and its effect on alpha rhythm power.

In all cases, alpha rhythm was investigated both at the parieto-occipital and frontal regions, to point out differences.

Finally, we wish to stress that a novel aspect of this work is the analysis of alpha rhythm in VR environment. The study was designed within the framework of the Horizon 2020 project CASTLE (CAbin Systems design Toward passenger welLbEing), aimed at optimizing the design of innovative interiors of aircraft cabins for Business Jet Industry, also exploiting VR for the collection of users’ feedback. Indeed, the present availability of sophisticate VR instruments now allows changes in brain rhythms to be studied when the subject is immersed in a complex realistic scenario and mimicked in a controlled repeatable condition. This idea opens new perspectives not only in the design of artificial systems, but also in the study of the human interaction with the external world.

2. Materials and Methods

Two experiments were carried out in the present study. They served to differently manipulate the task load (more/less demanding task) and direction of attention (internally/externally directed attention). The first experiment (Experiment 1) was performed in a controlled laboratory environment outside the VR setting; a classical monitor screen was used for stimuli presentation to participants and a wired EEG device was used for signal collection. Experiment 1 included two tasks that both required external and internal attentional components (intake of visual items for their internal manipulation) but with different task loads, one task being more demanding than the other. In the second experiment (Experiment 2), we took advantage of the VR technology to strongly manipulate the direction of attention. This experiment was conducted in a VR laboratory where participants were exposed to and interacted with a VR environment (aircraft business cabin interior), and a wireless EEG device was used for data collection. Experiment 2 involved a condition consisting in purely VR immersion whereby the rich sensory stimulation elicited external attention and a condition consisting in performing a mental task during the VR immersion; at variance with purely VR immersion, the latter condition required internal attention and neglect of external environment to perform the mental operations.

2.1. Participants

Thirty healthy volunteers (10 females), aged 20–42 years (mean ± std = 25.4 ± 4.8 years), took part in Experiment 1. Forty-one healthy volunteers (9 females), aged 19–29 years (mean ± std = 22.1 ± 2.6 years), took part in Experiment 2. Participants in the two experiments were different; this avoided that the participants were subjected to a long recording involving several sessions and conditions (of both experiments), that may have induced tiredness and boredom. Each participant had normal or corrected to normal vision and reported no medical or psychiatric illness. The study was approved by the local ethical committee (file number: 187339, year: 2018), and all participants gave written informed consent before the beginning of the experiment. All data were analyzed and reported anonymously.

2.2. Experiment 1: Cognitive Tasks Driven by External Stimuli and with Different Demand
2.2.1. Experimental Protocol

The participants comfortably seated facing a computer monitor at about 50 cm far, in a dedicated laboratory. They underwent two experimental sessions, each lasting 15 minutes, separated by a break of about 10 minutes (Figure 1(a)). Each experimental session consisted of three phases: a 5 min initial relaxation phase (named r1), a 5 min central task phase (named T), and a 5 min final relaxation phase (named r2). The two relaxation phases, preceding (r1) and following (r2) each task, were identical in both sessions: a gray screen with the word “relax” was continuously displayed (Figure 1(b)), and participants were instructed to relax during such phases maintaining the eyes open. The experimental sessions differed only in the type of the task executed during the central phase, namely, an arithmetic task and a reading numbers task (Figure 1). The order of the tasks was counterbalanced across participants. Both the implemented tasks involved exploration and intake of visual items (symbols and numbers) and their internal manipulation; thus, they involved both visual-spatial processes (external attentional component) and cognitive processes (internal attentional component), but the arithmetic task required higher level of sensory attention and cognitive effort.

(a)
(a)
(b)
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(1) The Arithmetic Task. During this task, the participants had to solve the arithmetic operations displayed on the screen, consisting in the addition/subtraction of four one-digit numbers, and had to compare the result with a given displayed target. They provided their response by selecting one of the three displayed button-items (black boxes with symbols < = >, see Figure 1(b)) using the mouse. Each operation was displayed on the computer monitor continuously until the participant responded; immediately after, the screen was updated displaying a new operation together with the target and the three response items (Figure 1(b)). Participants were instructed to answer not only as accurately as possible but also as quickly as possible, motivated by a timer that signaled the time left at each screen update (Figure 1(b)). For each arithmetic operation, the four one-digit numbers and the three operators (+ or −) were generated randomly; the comparison target was generated as a random integer close to the correct result of the arithmetic operation in order to avoid trivial solutions (the absolute difference between the comparison target and the correct result was ≤3).

(2) The Reading Numbers Task. During this task, the screen displayed the arithmetic operation, the comparison target, and the timer in order to provide similar visual items as in the arithmetic task, but the participants were clearly instructed to just mentally read the numbers presented on the screen, without performing any operation (response buttons were not displayed). The screen was updated every 5 seconds (Figure 1(b)). At each screen update, the numbers and operators in the arithmetic operation and the comparison target were generated randomly as in the arithmetic task.

Tasks similar to the ones implemented here were previously adopted in other studies to investigate attentional-related EEG rhythms modifications [39–41].

Before the onset of each experimental session, the participants received the instructions about the task of that session. During each session, participants were asked to reduce body and head movements at minimum (except finger movement for mouse use in the arithmetic task) and not to speak.

It is worth noticing that, in each session, the relaxation phase r1 was considered as the reference state within that session, and the alpha power modifications induced by the task in the following phases T and r2 were evaluated with respect to this reference state (see also Section 2.2.3). This was done to focus only on the changes induced by the specific task, ruling out other possible confounding effects (e.g., participant’s fatigue due to execution of the previous session).

2.2.2. EEG Recording and Preprocessing

During each experimental session, thirteen EEG signals were recorded via a wired, laboratory-grade device (Brainbox® EEG-1166 amplifier, Braintronics, The Netherlands and Neurowave Acquisition Software, Khymeia, Italy), using wet Ag/AgCl scalp electrodes (embedded in an elastic cap). Electrodes were located at positions F3, F4, T7, C3, Cz, C4, T8, PO7, PO3, PO8, PO4, O1, and O2; the reference electrode was placed on the right earlobe, and the ground electrode was located on the forehead. The number and positions of the electrodes were chosen as a trade-off between the following requirements. (i) Use of a restricted number of electrodes in an effort to outline a system characterized by ease of use, reduced setup time, and low cost, prospectively aimed at real-world practical applications. (ii) Allow coverage of both the frontocentral and parieto-occipital regions, the latter known to be more involved in visual-spatial (and computational) processing than the first [21, 39, 42, 43]. This may be useful to detect potential differences among scalp regions.

During each experimental session, the EEG signals were digitized at a sample frequency of 128 Hz and 16 bit resolution, and with the inclusion of a hardware notch filter eliminating line noise at 50 Hz. Then, for each participant, the two 15-minute EEG recordings, each relative to one of the two different sessions, were converted in a Matlab-compatible format for further offline processing (Matlab R2016a, The MathWorks Inc., Natick MA). First, each 15-minute recording was high-pass filtered at 0.75 Hz to eliminate the DC offset and slow drifts. Subsequently, we applied the Independent Component Analysis (ICA), an effective method largely employed for removal of artefacts from EEG [44–46]. For this purpose, each recording was entered into the “infomax” ICA algorithm (implemented by the EEGLAB toolbox) [47, 48]; artefactual Independent Components were visually identified and removed. An average of 3.87 ± 0.8 Independent Components were rejected across all participants and sessions. In particular, three rejected components were common across all recordings and separated three independent artefact activities inevitably present, i.e., eye blink, lateral eye movements, and heartbeat; one or two additional artefact components were occasionally present extracting EMG-related activity or single-channel noise.

2.2.3. Alpha Power Computation

For each participant and each session, the preprocessed EEG signals were subdivided into three parts of 5 minutes each, corresponding to the three phases of the session (r1, T, and r2). The Power Spectrum Density (PSD) of each channel over each phase was obtained by applying Welch’s periodogram method, by using a Hamming window of 5 seconds at 50% overlap, zeropadded to 10 s to obtain 0.1 Hz frequency resolution. Then, for each channel, the power in the alpha band 8–12 Hz was computed for each phase r1, T, and r2. Moreover, a normalization procedure was adopted. Specifically, in each session, the alpha power value of a single channel in the r1 phase was used as reference value for that channel, and the alpha power in each phase of the same session was divided by this reference value, to obtain the normalized alpha powers for that channel.

In addition to the analysis at single-channel level, we performed an analysis at scalp-region level, by aggregating the channels into two regions of interest: a region (fronto-central-temporal, FCT region) including the anterocentral channels (F3, F4, T7, C3, Cz, C4, and T8) and a region (Parieto-occipital, PO region) including the posterior channels (PO7, PO3, PO8, PO4, O1, and O2). To this aim, for each participant and each session, the mean PSD over the FCT and PO regions were computed by averaging the PSD across the corresponding channels, separately for each phase r1, T, and r2. Then, similarly to the single-channel analysis, the power in the alpha band 8–12 Hz was computed over each region and for each phase r1, T, and r2. Finally, the normalized alpha powers at the scalp-region level were computed: the alpha power in the r1 phase over a region was used as the reference value for that region, and the alpha power value in each phase over the same region was divided by this reference value. Of course, the normalized alpha powers assumed value 1 in the r1 phase, both at single-channel level and at scalp-region level.

2.3. Experiment 2: VR Immersion and Mental Task in VR Immersion
2.3.1. Virtual Reality Instrumentation and the Aircraft Virtual Cabin Interiors

The concept and the CAD (Computer Aided) model of the cabin interiors of a business jet were provided by ACUMEN (https://acumen-da.com/). The model design is based on a modular layout of the cabin that is divided into five zones, and for each zone, different functional requirements have been defined by Dassault Aviation. There is a flexible area for informal and formal activities. Moreover, there is a rear cabin area designed with enough privacy and discretion as main targets. The central lavatory is between the two flexible zones and is expected to be easy to access to and safe to use. Finally, the galley and the crew rest areas are provided, all referenced in the fuselage model. The surface CAD model was processed in IC.IDO (Industrial Grade Immersive VR Solutions) Software to create the digital mock up of the entire cabin with the proper color, material and finishing properties for each visible surface. IC.IDO® is a 3D immersive VR software, provided by ESI® Group, supporting industrial decision making processes and digital mock-up verifications (Figure 2(a)). Then, two different CMF (color, material, and finishing) configurations of this cabin model were prepared for test (Figures 2(b)–2(c)), namely, configurations B1 and B2.

The cabin model files, properly converted and refined, were deployed on the CAVE (Cave Automatic Virtual Environment) at the Virtual Reality Laboratory of the University of Bologna. The CAVE is a multiple screens stereoscopic visualization system that immerses the user in a virtual environment [49]. The CAVE is developed on top of Commercial of The Shelf (COTS) components and is based on three 2.5 × 1.9 m rear-projected screens and a floor. The active stereoscopy was enabled through shutter glasses. To allow the cabin environment to be navigated from a first-person perspective by a user moving on the CAVE floor, face and body tracking was implemented by capturing and filtering data provided by a Microsoft Kinect sensor placed in front of the user at the bottom of the CAVE central screen. Tracking of the face was used to update the VR camera’s point of view with the actual user’s point of view [50]. Body tracking allows the longitudinal navigation of the cabin, implemented through the amplification of the user’s step distance in the main axis direction. In addition, an avatar representing the user was introduced in the cabin virtual environment, and the avatar’s joints and face position and orientation were linked to the user’s ones captured by Kinect, so that the avatar replicated user’s movements and gestures (Figure 2(d)). Finally, to simulate interaction with objects of the virtual environment, a sound was produced by the system whenever the avatar hurt or touched them, to fake collision.

2.3.2. Experimental Protocol

The participants underwent two experimental sessions within the VR laboratory, one for each virtual cabin configuration, B1 and B2, separated by a break of about 10 minutes (Figure 2(e)). The order of the presentation of the two configurations was counterbalanced across participants. It is worth noticing that the replication of the session using two virtual configurations of the same environment served to test the robustness of the adopted EEG measure (alpha power) and of its extraction procedure. Indeed, as the two configurations differed in subtle details (color and finishing), we expected similar sensory stimulation to be elicited by immersion in them and therefore similar effects on alpha power to be observed across the sessions (see also Sections 2.3.4 and 3.2.1).

Throughout each session, lights were kept off to improve clearness and contrast of the images projected on the CAVE screens and favor participants’ immersion within the VR environment; moreover, a background airplane sound was played continuously. All participants were required to not speak throughout the sessions.

Each session was structured into 5-minute phases. The two sessions shared the same structure with the exception that 24 out of the 41 participants performed an additional phase (maVR) in the second session (with either the B1 or B2 virtual configuration, see Figure 2(e)). This additional phase served to test the effects of an internal task (mental arithmetic) requiring isolation from the realistic external context. The remaining phases were common across the two sessions for all participants (Figure 2(e)). The first 5-minute phase, named r1, consisted in an initial VR-off relaxation phase without VR stimulation (and only the background sound on); during this phase, the participants were seated centrally in front of the black screens at a distance of about 2 meters and were instructed to relax with eyes open, while the VR environment was kept off. Immediately at the end of this phase, the VR environment was turned on and was kept on until the end of the session. The second 5-minute phase, named r1VR, consisted in a first still (static) VR immersion: during this phase, the participants remained seated while being immersed in a static VR scenario, showing the cabin lounge and conference room (flexible area), and were solicited by the rich sensory stimuli and free to visually explore the virtual scenario (via eye and head movements). The third 5-minute phase, named intVR, consisted in an interactive VR exploration: during this phase, the participant stood up, walked, moved, and interacted through the virtual cabin interior, trying to explore all the zones. The fourth 5-minute phase, named r2VR, consisted in a second still (static) VR immersion, following the interaction phase: during this phase, the same conditions as in the r1VR phase were replicated, with the participants seating again immersed in the same static scenario shown previously. The additional phase maVR performed by the subset of participants consisted in a mental arithmetic task executed during the VR immersion: during this phase, the participant remained seated immersed in the same scenario as in r1VR and r2VR and performed mental serial subtractions in steps of seventeen starting from 1000.

In this study, we did not employ a realistic seat (i.e., similar to the ones present in a real cabin) during the phases in which participants remained seated; of course, this improvement could be implemented in future studies to further enhance the VR experience.

The relaxation phase r1 in each experimental session was considered as the reference condition within that session, and the modifications induced by the VR immersion as well as by the mental arithmetic task (phases r1VR, r2VR, and maVR) were evaluated with respect to this reference state (see also Section 2.3.4), to exclude possible bias due to execution of the previous session. It is worth noticing that, for each session, the interactive exploration phase, intVR, was excluded from the analysis (see also Section 2.3.3). Indeed, this phase mainly included motor aspects which fall outside the focus of the present study (moreover, this analysis would be particularly complex as removing locomotion-induced mechanical artifacts from EEG signals in a reliable way is still a critical problem). Rather, the interaction phase might be useful to assess whether alpha power was modified before and after an active exploration of the VR environment, possibly reflecting a modification of external attention level.

2.3.3. EEG Recording and Preprocessing

In this experiment, a wireless consumer-grade EEG device was used to acquire the EEG signals. Specifically, we employed the OpenBCI Cyton board complemented with the OpenBCI Daisy Module (OpenBCI, https://openbci.com/) that allows up to 16 differential EEG channels to be acquired wirelessly via the OpenBCI USB transmitter/receiver using RFduino radio module. The use of a wireless device was fundamental for EEG recording in the VR laboratory, eliminating restrictions on positioning the participants inside the laboratory and allowing free movements and mobility of the participant when immersed in the VR scenario.

Twelve wet Ag/AgCl electrodes (F3, F4, T7, C3, C4, T8, PO7, PO3, PO8, PO4, O1, and O2) of an electrode cap were plugged into the differential channels of the OpenBCI Cyton + Daisy Board, and the board was secured over the cap in the central position, so as to realize a wireless and wearable system. The same electrodes as in Experiment 1 were used, except electrode Cz skipped for board fixing. The reference electrode was placed on the right earlobe and the ground (bias) electrode was placed on the left earlobe.

For each participant and during each experimental session, the twelve EEG signals were online digitized at a sample frequency of 125 Hz and 24 bit resolution and stored in a Matlab-compatible format. Then, each recording was offline preprocessed. First, each recording was high-pass filtered at 0.75 Hz to eliminate the DC offset and slow frequency drifts and filtered by a 50 Hz notch filter to eliminate line-power interferences. Then, the portion of the signals corresponding to the interaction phase (intVR, from minute 10 to minute 15) was excluded from any further analysis, and the signals in the remaining phases (r1, r1VR, r2VR, and the additional phase maVR for the subset of participants) were examined for artefacts reduction. At variance with Experiment 1, ICA applied to signals acquired in Experiment 2 was in general unable to separate artefactual activities. The reason was due to the different recording modality and device (wireless vs. wired and consumer-grade vs. laboratory grade) and different experimental conditions (participants free to move head, neck, and possibly even trunk to explore the wide facing screens vs. participants facing 15 inches monitor and instructed to reduce their movements to a minimum). As a consequence, several nonstereotypic types of noise, such as complex movement artifacts, electrode pops, transient reduction, and loss of signal transmission, affected signals in Experiment 2, besides more stereotypical artefacts (such as blinking or heartbeat related artefacts). Since only twelve ICs were returned as output, the manifold single-artefactual activities were spread over several (or even all) components, mixing with the useful signal components. Therefore, to reduce artefact effects (especially those induced by less stationary activities), we opted for a direct visual inspection of each EEG recording, removing those fragments containing muscle activities, movement artefacts, electrode artefacts, and transient lost/decreased transmission (removal was obtained by just concatenating the preserved portions). The average number of removed fragments was 2.12 ± 3.1 with a mean duration of 32 s, across participants and sessions. While the ineffectiveness of ICA may be considered a limit, this also hints practical implications. Indeed, this suggests that other procedures for artefact removal are more apt to be used in a low-density, wireless, and wearable system (and in real-world applications) and more susceptible to an online implementation, rather than ICA that requires training using sufficiently long and stationary signals.

2.3.4. Alpha Power Computation

We implemented alpha power computation for the entire set of participants (41) over the two sessions and an additional computation over the second session for the subset of participants (24) who performed the additional maVR phase.

(1) Alpha Power Computation over the Entire Set of Participants (41) and the Two Sessions (Phases r1, r1VR, and r2VR). This analysis served to assess the effect of purely VR immersion on alpha power. For each participant and each session, the preprocessed EEG signals were subdivided into three parts, corresponding to the three phases r1, r1VR, and r2VR (each part lasted approximately 5 minutes depending on the removed fragments), which were the phases performed by all participants in both sessions. Hence, the PSD of each channel over each phase was obtained by applying Welch’s periodogram method, adopting the same parameters as in Experiment 1. The power in the alpha band 8–12 Hz was computed both at single-channel level and scalp-region level, according to the same procedure as in Experiment 1. Here, the fronto-central-temporal region was obtained by aggregating six (F3, F4, T7, C3, C4, and T8) rather than seven electrodes, as the Cz electrode was not used (see Section 2.3.3). As in Experiment 1, for each participant and for each experimental session, the alpha power values of each single channel/region in the three phases (r1, r1VR, and r2VR) were divided by the corresponding reference value (i.e., the alpha power in the r1 phase), to obtain the normalized alpha powers and to assess the alpha power modifications with respect to the reference state (r1).

Moreover, across all 41 participants, we included a further analysis to evaluate alpha power modifications at a finer time resolution. To this aim, for each participant and session, the first 10 minutes of the session (comprising the consecutive phases r1 and r1VR) were subdivided into 1-minute segments, and the alpha power over the FCT and PO scalp regions was computed with 1-minute time resolution. In this analysis, we still adopted a normalization by division using the alpha power value obtained in the first minute of the session (i.e., the first minute of the r1 phase) as the reference value.

It is important to note that the computations above were performed separately over each session obtaining separated values of normalized alpha powers for the B1 configuration and B2 configuration. In a preliminary analysis, we did not found significant differences in the B1 vs. B2 normalized alpha powers at any phase and region, in line with our expectations based on the limited dissimilarities between the two configurations. Therefore, the normalized alpha powers for the B1 and B2 configurations were collapsed together; to this aim, we computed the average alpha power across the two configurations for each participant. The collapsed values are shown in Results and used for subsequent statistical analyses (see Section 2.4).

(2) Alpha Power Computation over the Subset of Participants (24) in the Second Session (Phases r1, r1VR, r2VR, and maVR). For these participants, we added a further analysis limited to the second session that included the phase maVR. This analysis served to assess how alpha power was modulated when a mental process required inward shift of attention and isolation from the environment. For each participant, the power in the alpha band 8–12 Hz was computed at scalp-region level in the four phases r1, r1VR, r2VR, and maVR of the second session. Then, for each participant and region, the alpha powers in these phases were divided by the corresponding reference value (i.e., the alpha power in phase r1), to obtain the normalized alpha powers.

2.4. Statistical Analyses

In both Experiments, the variable under statistical tests was the normalized alpha power obtained at the scalp-region level. For each experiment, the differences between the reference value (1) and the other phases (or times, in case of the analysis at 1 min time resolution) were tested via multiple one-sample t-tests, separately within each region, using Bonferroni correction (significance threshold = 0.05/n, where n was the number of comparisons). Moreover, the normalized alpha power was analyzed via repeated measures two-way Analysis of Variance (ANOVA). In Experiment 1, we analyzed the variable at the phase T and the within subject factors were: Task Type (arithmetic/reading numbers) and Region (FCT/PO). In Experiment 2, the within subject factors were: Phase (r1VR/r2VR for the variable computed on the entire set of participants; r1VR/r2VR/maVR for the variable computed on the subset of participants) and Region (FCT/PO). Post hoc comparisons were performed via pairwise t-tests with Bonferroni correction (significance threshold = 0.05/n, where n was the number of comparisons). For clarity, in one-sample and paired t-tests uncorrected values were reported, together with the adjusted significance threshold.

3. Results

3.1. Experiment 1: Effect of Cognitive Tasks Driven by External Stimuli and Different Task Demands

Figure 3 shows the topographical scalp maps of the alpha power (not normalized) averaged across participants as a function of the experimental session (arithmetic and reading numbers session) and of the phase (r1, T, r2) within the session. In both sessions, the pretask relaxation phase (r1) was characterized by a large predominance of alpha power over the posterior area and a gradual decline towards the frontal-central regions (Figures 3(a) and 3(d)). During the task phase (T), the alpha power exhibited a widespread reduction, larger over the posterior area than over the frontocentral area; moreover, the arithmetic task (Figure 3(b)) induced a stronger alpha power decrease than the reading numbers task (Figure 3(e)). Finally, during the posttask relaxation phase (r2), the alpha power distribution resumed a similar pattern as in the r1 phase, with the alpha power increasing up to values slightly above the pretask phase (see Figures 3(c) and 3(f)).

A straightforward quantification of the task-induced alpha power changes across the electrodes was obtained via the normalized alpha power at the single-channel level. Figure 4 displays the normalized alpha power at each electrode, averaged across participants (mean ± sem), plotted during the task (T), and after the task (r2) in the arithmetic and reading numbers sessions. Thus, in this plot value 1 represents the pretask reference value for each electrode. The alpha power exhibited a larger decrease (by about 0.15 points) during the arithmetic task than that during the reading numbers task across all electrodes (solid red and blue lines). Furthermore, in the task phase (both for arithmetic and reading numbers), an abrupt reduction in the normalized alpha power was evident at the transition from the fronto-central-temporal electrodes to the parieto-occipital electrodes. During the posttask relaxation phase (r2 and dotted red and blue lines), the normalized alpha power assumed similar values across the electrodes and sessions, slightly overcoming the pretask value.


The analysis at scalp-region level is presented in Figure 5 that depicts the normalized alpha power computed over the two scalp regions (FCT: Figure 5(a); PO: Figure 5(b)), in the three phases of the two experimental sessions (arithmetic/reading numbers). The values at phase T emphasize the stronger effect of the arithmetic task compared to the reading numbers task in reducing the alpha power within each region and the larger alpha power decrease in the PO region (Figure 5(b)) than that in the FCT region (Figure 5(a)) during each task. Multiple one-sample t-tests (Figure 5) confirmed that the normalized alpha power significantly deviated from the r1 reference value (1) during the task phase (both the arithmetic and reading numbers task), but not during the r2 phase, within each region. The 2 × 2 repeated measures ANOVA conducted on the normalized alpha power in T (factors: Task Type = arithmetic/reading and Region = FCT/PO) revealed that there was a main effect of Region (F(1,29) = 23.9, ), showing that the alpha power decreased more posteriorly than anteriorly during the tasks. Moreover, there was a main effect of Task Type (F(1,29) = 24.1, ) showing that the arithmetic task induced a larger alpha desynchronization than the reading numbers task.

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3.2. Experiment 2: VR Immersion and Mental Task in VR Immersion
3.2.1. Effect of the VR Immersion

This section presents the results obtained across the entire set of 41 participants, on phases r1, r1VR, and r2VR, showing the effects of the VR immersion in absence of any specific task. It is worth noticing that the displayed results concern the alpha power values over the two VR cabin sessions aggregated together (see Section 2.3.4 in Materials and Methods): indeed, the two virtual experiences turned out to be characterized by highly similar patterns of alpha powers (not shown results). This was an important preliminary outcome as it proved robustness of the adopted EEG measure and of its extraction procedure, attesting that similar VR configurations (hence similar visuospatial stimulations) induced similar alpha power changes.

The analysis at single-channel level is shown in Figure 6; it plots the normalized alpha power at each electrode (mean ± sem across participants), during the phases of pure VR immersion (r1VR and r2VR). The following main observations can be drawn. First, the alpha power during the first visual exploration (r1VR, preinteraction) exhibited larger decrease than during the second visual exploration (r2VR, postinteraction) across all electrodes. Moreover, an abrupt decrease in the normalized alpha power occurred at the transition from the fronto-central-temporal to the parieto-occipital channels, both in r1VR and r2VR, while the electrodes within each set exhibited close values, similarly to what observed in Experiment 1 (Figure 4).


Motivated by the previous differences, an analysis at scalp-region level was performed in this case too.

Figure 7 shows the PSD over each scalp region (FCT region: Figure 7(a); PO region: Figure 7(b)) averaged across participants, and computed separately for each phase. The PO region (Figure 7(b)) was characterized by a huge peak in alpha band in the reference state r1 that dramatically decreased during r1VR and r2VR, while the FCT region (Figure 7(a)) presented a lower alpha peak and smaller modulation of its amplitude.

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The obtained values of the normalized alpha power (mean ± sem across participants) in the three phases (r1, r1VR, and r2VR) are depicted in Figure 8, for each region separately (FCT: Figure 8(a); PO: Figure 8(b)). The VR immersion was characterized by a larger alpha power modulation over the PO region (Figure 8(b)) than the FCT region (Figure 8(a)). It is interesting to note that by comparing Figures 8 and 5, the alpha power desynchronization in VR immersion appeared to assume values similar to those observed in the reading numbers task rather than the arithmetic task, over both scalp regions. Multiple one-sample t-tests (Figure 8) confirmed a significant deviation of the normalized alpha power from the reference value (1) in both phases r1VR and r2VR, within each region. The 2 × 2 repeated measures ANOVA (factors: Phase = r1VR/r2VR and Region = FCT/PO) revealed that there was a main effect of Region (F(1,40) = 29.32, ) showing that alpha power decreased more posteriorly than anteriorly during the VR immersion. Moreover, there was a main effect of Phase (F(1,40) = 15.01, ), indicating that alpha exhibited a larger desynchronization in the preinteraction static immersion (r1VR) than that in the postinteraction static immersion (r2VR).

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Furthermore, we tested whether the alpha power index was able not only to capture differences among distinct 5-minute phases, but also to monitor trends and variations with a higher temporal resolution (1 minute), to promptly detect a change in the state of the participant at the transition from one phase to another. Figure 9 plots the temporal pattern, at 1 min resolution, of the normalized alpha power (mean ± sem across participants) throughout the first ten minutes of the sessions (comprising phases r1 and r1VR), over each region (FCT: Figure 9(a); PO: Figure 9(b)). An interesting pattern emerged especially in the PO region (Figure 9(b)). In this region, alpha power exhibited an evident secondary increase after the first minute of the r1 phase. This pattern may reflect a progressive relaxation in the very first period of phase r1, when the participants were seated down and got used to the experimental setup. A large and abrupt alpha power decrease (evident also in the FCT region) occurred at minute 6, as soon as the participant got immersed in the VR environment, as an evident marker of visual stimulation and capture of external attention by the immersive sensory inputs. In the following minutes (minutes 7–10), the alpha power tended to moderately increase suggesting a gradual lessening of attention as the immersion in the static VR environment went on. Figure 9 also displays the results of the multiple one-sample t-tests contrasting the normalized alpha power at each minute with the reference value (1), within each region. Almost all time points satisfied the uncorrected significance threshold (0.05, ), except minutes 4, 5, and 9 in the FCT region. Interestingly, minutes from 6 to 8 (and even minute 9 in the PO region) survived the Bonferroni corrected threshold (0.05/9, §).

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3.2.2. Effect of an Internal Cognitive Task in VR Immersion

This section presents the results obtained across the subset of 24 participants, in the phases r1, r1VR, r2VR, and maVR of the second session, showing how the alpha power was modified when shifting from a condition of external attention to a condition requiring internal attention against the external appealing environment.

Figure 10 displays the normalized alpha power (mean ± sem across the 24 participants) in the four phases, separately for the two regions (FCT: Figure 10(a); PO: Figure 10(b)). As well expected, the alpha power exhibited a decrease in both phases r1VR and r2VR, more evident in the PO region, similarly to the effects previously observed across all participants and sessions (Figure 8). Here, it is remarkable the dramatic increase in the alpha power induced by the execution of the mental arithmetic during the VR immersion. In particular, in this condition, the alpha power assumed values very close to the reference value, i.e., to the initial relaxation condition (r1). Indeed, the one-sample t-tests confirmed that the normalized alpha power during maVR did not deviate from the reference value (1), at variance with the phases r1VR and r2VR (Figure 10). The 3 × 2 repeated measure ANOVA (factors: Phase = r1VR/r2VR/maVR and Region = FCT/PO) disclosed that there was a significant Phase × Region interaction (F(2,46) = 10.77, ) and a main effect of Phase (F(2,46) = 9.299, ). Indeed, post hoc t-tests revealed that the alpha power was lower in the PO region than FCT region in both phases r1VR and r2VR ( in both phases, corrected significance threshold = 0.05/3 = 0.0167), whereas no difference across the two regions emerged in the maVR phase (). Moreover, the alpha power in each phase, r1VR and r2VR, was lower than in maVR ( and , respectively; corrected significance threshold = 0.05/3 = 0.0167).

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4. Discussion

The present results provide several interesting indications, which not only may contribute to our understanding of the role of alpha oscillations and of the mechanisms driving alpha increase/decrease, but can also have practical perspectives in future studies oriented to the noninvasive assessment of human/environment interaction via scalp EEG.

4.1. Electrodes Position

First, all electrodes in the scalp exhibited a significant ERD in the alpha band, both during the laboratory tasks (Experiment 1) and during the pure VR immersion. However, the level of ERD was significantly stronger in the parietal-occipital electrodes compared to the frontal-central ones. In particular, in these experiments, a drastic fall in alpha power was evident passing from the frontal-central to the parietal-occipital electrodes (Figures 4 and 6). This difference was even more evident using absolute values of power instead of normalized ones (Figure 3). This result agrees with results of several neurocognitive works. Indeed, recent EEG studies suggest that parietal and occipital regions are involved in visuospatial processing of stimuli [15, 42], spatial representations of numbers [51], and arithmetic problems [39, 43], at least when the latter involved external attention and visual processing too (such as the arithmetic task of the Experiment 1). It is probable, however, that other kinds of tasks (for instance those requiring motor actions or working memory) rely more on frontal-central regions [25, 52] and on other rhythms such as theta, beta, or gamma [53]. An interesting point is that the same electrodes (PO3 and PO4) were mainly sensitive both in the laboratory cognitive tasks and in the VR immersion (Figures 4 and 6). This provides the indication that, at least in this kind of problems, the number of electrodes can be significantly reduced without a significant loss in method sensitivity, thus further dropping the complexity of the experimental setup and improving its portability in real scenarios.

4.2. ERD and Attention

As we anticipated above, results of the present study confirm several data in the literature; however, they also introduce some interesting new elements. (1) First, we confirmed that attention to visual stimuli (either in the reading numbers task or in the Virtual Reality immersion) causes a significant ERD compared with a previous relaxation phase, especially accentuated in the parieto-occipital regions. Although various authors observed ERD in response to visual engagements [9–14], this is the first time that visual intake is not produced by specific stimuli, but via a full immersion in a motivating VR environment. This signifies that VR environments can represent a new important tool to study human internal vs. external attention in future work, more similar to conditions occurring in real life. (2) In Experiment 1, we differentiated the effect of a simpler visual task (pure reading numbers) and a more complex task (reading + arithmetic operation) which still involved external attention but a higher internal processing. In fact, from the previous analysis of the literature, it is still not clear whether and in which conditions an increase in the internal task produces ERS or ERD. Our results indicate that ERD was more accentuated during the demanding task (arithmetic operation), i.e., the arithmetic computation (although internal) further reduced alpha power, provided the task was driven by external visual inputs (attention to the digits). This result means that the alpha-band power can be finely modulated by the level of external attention and that external attention (not the task load) is the dominant factor in visual tasks. This result agrees with previous studies [3, 8]. Moreover, Schupp et al. [23] observed lower alpha for perceptual tasks as opposed to purely mental tasks. In agreement with our result, Benedek et al. [54] suggest that task processing under low internal processing demands (i.e., involving bottom-up processing) did not result in alpha synchronization but rather in strong desynchronization, especially in posterior brain regions, which could reflect stronger demands on the visual system. Only during more demanding tasks, involving top-down control and creativity, can ERS be verified. This result apparently disagrees with a result by Cooper et al. [21], who observed an increase in alpha power with the task demand not only during internal, but also during external attention tasks. We think that these differences may depend on the fact that, during Cooper et al. experiments, some items, given in sequence, should be maintained in memory for a certain period, whereas in our experiment, all numbers were simultaneously available and the external input stream dominated the process. In conclusion, our original result is that an internal arithmetic task can produce ERD, if dominated by external attention. (3) At odds with the previous experiment, in the VR experiment (maVR phase), we used an arithmetic task which was merely mental, while the strong visual intake (cabin immersion) had no relation with the task. At the same time, we did not use specific distractors, but the overall full immersion in the cabin environment had a distractor function. In this condition, we demonstrate that alpha power returned to approximately the same level (or in some participants, even to a higher level) as in the initial resting condition. This result agrees with previous studies, showing that alpha activity increases during a purely mental task not driven by sensory inputs [20, 22, 24]. A difference from previous studies, however, is that we started the mental task from a condition in which alpha power was already significantly reduced by attention to the cabin. We are not aware of any similar experiment performed before (i.e., a global environment distractor). It is interesting to observe that alpha power returned toward baseline (i.e., the resting state), suggesting that the participant was trying to completely neglect the VR environment, i.e., to reach a complete isolation state. This result suggests that the alpha power increase has the most important function to isolate a subject from the external world.

4.3. Artefact Removal

EEG signals are commonly affected by artefacts. Hence, artefact removal is an important aspect of any EEG processing method. Today, ICA is probably the most employed method for removal of artefactual activity from EEG cerebral signals [44], being highly effective in separating several stereotyped nonbrain artefacts (eye blinks, eye movement potentials, EMG, and ECG) from the rest of EEG signals, given that they represent independent physical processes. For this reason, we used this classical and consolidated technique to effectively remove artefacts from the EEG recording acquired in the controlled laboratory setting (Experiment 1) via the wired, laboratory-grade device. However, accurate EEG artefact removal in environments outside controlled laboratory settings, in real or realistic scenarios, and/or in online applications, is still a critical open issue. In these less-controlled conditions, indeed, several nonstereotyped and transient artefacts may corrupt the EEG signals, and ICA may result ineffective in separating them if not sufficient stationary time points are provided. Indeed, we encountered this problem in our recordings obtained in the VR environment with the wireless, consumer-grade EEG device: a large number of artefactual elements (including several nonstereotyped activities) were mixed over most or all ICs, making it impossible to separate the useful signal from the spurious noise via a simple IC selection. This problem is further aggravated (as in our recordings) when a limited number of EEG channels is acquired, as the number of estimated ICs, in the basic ICA model, is constrained to be equal to the channel number (thus imposing a superior limit to the number of independent signals that can compose the mixed EEG for their efficient separation). On the other hand, a limited number of electrodes is a desirable feature in real applications reducing the time of preparation and cost. Due to the ineffectiveness of ICA, in case of the VR recordings, we simply eliminated the EEG portions affected by too much noise from the signal processing procedure: portions with too much noise were not examined and did not contribute to the final analysis. Results, however, were still quite robust and reliable as shown in Figures 6–9. Moreover, the robustness of our procedure for EEG processing in VR recordings was further supported by our preliminary analysis performed on the results obtained separately in the B1 and B2 virtual configurations; this analysis (not shown results) verified that the two virtual configurations, pretty similar and thus eliciting similar visuospatial sensory stimulation, induced overlapped patterns of EEG alpha powers. However, our study confirms that EEG artefactual removal is still a crucial problem in real-world or realistic applications. This problem is currently faced by the scientific community and new methods, also more online-capable than ICA, for removal of transient, and nonstereotyped artefacts have been recently suggested [44, 55]. An important development of the present study will concern testing alternative and more recent methods other than ICA for artefact correction of the VR recordings.

4.4. Temporal Aspects

During the VR immersion, the participant experienced a phase in which he/she was fully immersed in the cabin environment (r1VR), simply sitting down as a passenger during a travel, followed by a second phase in which he/she moved along the environment interacting with the objects (intVR). Then, a third phase followed, in which he/she seated again in a relaxed state fully immersed in the visual and acoustic cabin details (r2VR). We did not use the EEG registered during the interaction with the cabin, since the rapid body and head movements produced too much artefact noise on the electrode signals. However, as anticipated above, it is interesting to observe that, in the third phase of the measurement (r2VR), when the participant sat again after the active interaction, alpha ERD was less evident compared with the first phase, and this difference was statistically significant (see Results in Figure 8 and corresponding ANOVA). This may indicate that the attention-grabbing effect that the VR scenario caused on the participant partially declined as the participant became more used to the environment.

Sours: https://www.hindawi.com/journals/cin/2019/7051079/
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Alpha wave

Neural oscillations in the frequency range of 8–12 H

Not to be confused with Alpha particle.

For the 3D platform video game, see Alpha Waves.

Exclamation mark with arrows pointing at each other

This article or section appears to contradict itself on the origin of alpha waves. Please see the talk page for more information.(August 2020)

Alpha waves or Alpha rhythm are macroscopic neural oscillations in the frequency range of 8–12 Hz[1] likely originating from the synchronous and coherent (in phase or constructive) electrical activity of thalamic pacemaker cells in humans. Historically, they are also called "Berger's waves" after Hans Berger, who first described them when he invented the EEG in 1924.[2]

Alpha waves are one type of brain waves detected by electrophysiological and closely related methods, such as by electroencephalography (EEG) or magnetoencephalography (MEG), and can be quantified using quantitative electroencephalography (qEEG). They can be predominantly recorded from the occipital lobes during wakeful relaxation with closed eyes and were the earliest brain rhythm recorded in humans.[3] Alpha waves are reduced with open eyes, drowsiness and sleep. Historically, they were thought to represent the activity of the visual cortex in an idle state. More recent papers have argued that they inhibit areas of the cortex not in use, or alternatively that they play an active role in network coordination and communication.[4] Occipital alpha waves during periods of eyes closed are the strongest EEG brain signals.[citation needed]

An alpha-like variant called a mu wave can be found over the primary motor cortex.[citation needed]

Research[edit]

Possible types and origins[edit]

Some researchers posit that there are at least two forms of alpha waves, which may have different functions in the wake-sleep cycle.

Alpha waves are present at different stages of the wake-sleep cycle.[5] The most widely researched is during the relaxed mental state, where the subject is at rest with eyes closed, but is not tired or asleep. This alpha activity is centered in the occipital lobe,[6][7] although there has been speculation that it has a thalamic origin.[8] This wave begins appearing at around four months, and is initially a frequency of 4 waves per second. The mature alpha wave, at 10 waves per second, is firmly established by age 3.[9]

The second occurrence of alpha wave activity is during REM sleep. As opposed to the awake form of alpha activity, this form is located in a frontal-central location in the brain. The purpose of alpha activity during REM sleep has yet to be fully understood. Currently, there are arguments that alpha patterns are a normal part of REM sleep, and for the notion that it indicates a semi-arousal period. It has been suggested that this alpha activity is inversely related to REM sleep pressure.[citation needed]

It has long been believed that alpha waves indicate a wakeful period during sleep.[citation needed] This has been attributed to studies where subjects report non-refreshing sleep and have EEG records reporting high levels of alpha intrusion into sleep. This occurrence is known as alpha wave intrusion.[10] However, it is possible that these explanations may be misleading, as they only focus on alpha waves being generated from the occipital lobe.[citation needed]

Meditation[edit]

Mindfulness meditation has been shown to increase alpha wave power in both healthy subjects and patients.[11] Practitioners of Transcendental Meditation have demonstrated a one Hertz reduction in alpha wave frequency relative to controls.[12]

Alpha wave intrusion[edit]

Alpha wave intrusion occurs when the alpha waves appear with non-REM sleep when delta activity is expected. It is hypothesized to be associated with fibromyalgia with increased phasic alpha sleep activity correlated with clinical manifestations of fibromyalgia, such as longer pain duration.[13]

Despite this, alpha wave intrusion has not been significantly linked to any major sleep disorder, including chronic fatigue syndrome, and major depression. However, it is common in chronic fatigued patients, and may amplify the effects of other sleep disorders.[14]

Mistake prediction[edit]

Following this lapse-of-attention line of thought, a recent study indicates that alpha waves may be used to predict mistakes. In it, MEGs measured increases of up to 25% in alpha brain wave activity before mistakes occurred. This study used common sense: alpha waves indicate idleness, and mistakes are often made when a person is doing something automatically, or "on auto-pilot", and not paying attention to the task they are performing. After the mistake was noticed by the subject, there was a decrease in alpha waves as the subject began paying more attention. This study hopes to promote the use of wireless EEG technology on employees in high-risk fields, such as air traffic controlling, to monitor alpha wave activity and gauge the attention level of the employee.[15]

Measurement[edit]

EEG artefacts[edit]

As demonstrated by Dr. Adrian R. M. Upton, it is possible for extraneous sources (ambient fluctuations detected with a mound of Jell-O in Upton's experiments) to cause signals to appear on an EEG readout, causing false signals to be interpreted as healthy alpha waves. This finding suggests that it is possible that a non-flat EEG could lead to the interpretation that a patient is still living when in fact he or she is long dead.[16]

Cecil Adams from The Straight Dope discusses this scenario:

Sometimes it's claimed Jell-O brainwaves are identical to a healthy adult's. That's clearly a stretch, but the Jell-O EEG readings do look pretty similar to a normal human alpha rhythm. Alpha waves are observed when a patient is awake and resting with eyes closed, and in some kinds of sleep and reversible coma. True, the Jell-O waves are a little slower and of much lower amplitude, barely within normal human limits, but that doesn't tell you much by itself. Hypoxia, encephalitis, and other medical conditions can cause reduced frequency and amplitude, as can drug use.[17]

History[edit]

The sample of human EEG with prominent alpha-rhythm in occipital sites
The sample of human EEG with prominent alpha-rhythm in occipital sites

Alpha waves were discovered by German neurologistHans Berger, the inventor of the EEG itself. Alpha waves were among the first waves documented by Berger, along with beta waves, and he displayed an interest in "alpha blockage", the process by which alpha waves decrease and beta waves increase upon a subject opening their eyes. This distinction earned the alpha wave the alternate title of "Berger's Wave".[citation needed]

Berger took a cue from Ukrainian physiologist Vladimir Pravdich-Neminsky, who used a string galvanometer to create a photograph of the electrical activity of a dog's brain. Using similar techniques, Berger confirmed the existence of electrical activity in the human brain. He first did this by presenting a stimulus to hospital patients with skull damage and measuring the electrical activity in their brains. Later he ceased the stimulus method and began measuring the natural rhythmic electrical cycles in the brain. The first natural rhythm he documented was what would become known as the alpha wave. Berger was very thorough and meticulous in his data-gathering, but despite his brilliance, he did not feel confident enough to publish his discoveries until at least five years after he had made them. In 1929, he published his first findings on alpha waves in the journal Archiv für Psychiatrie. He was originally met with derision for his EEG technique and his subsequent alpha and beta wave discoveries. His technique and findings did not gain widespread acceptance in the psychological community until 1937, when he gained the approval of the famous physiologist Lord Adrian, who took a particular interest in alpha waves.[18]

Alpha waves again gained recognition in the early 1960s and 1970s with the creation of a biofeedback theory relating to brain waves (see below). Such biofeedback, referred to as a kind of neurofeedback, relating to alpha waves is the conscious elicitation of alpha brainwaves by a subject. Two researchers in the United States explored this concept through unrelated experiments. Joe Kamiya, of the University of Chicago, discovered that some individuals had the conscious ability to recognize when they were creating alpha waves, and could increase their alpha activity. These individuals were motivated through a reward system from Kamiya. The second progenitor of biofeedback is Barry Sterman, from the University of California, Los Angeles. He was working with monitoring brain waves in cats and found that, when the cats were trained to withhold motor movement, they released SMR, or mu, waves, a wave similar to alpha waves. Using a reward system, he further trained these cats to enter this state more easily. Later, he was approached by the United States Air Force to test the effects of a jet fuel that was known to cause seizures in humans. Sterman tested the effects of this fuel on the previously-trained cats, and discovered that they had a higher resistance to seizures than non-trained cats.[citation needed]

Alpha wave biofeedback has gained interest for having some successes in humans for seizure suppression and for treatment of depression.[19]

Alpha waves again gained interest in regards to an engineering approach to the science fiction challenge of psychokinesis, i.e. control of movement of a physical object using energy emanating from a human brain. In 1988, EEG alpha rhythm was used in a brain–computer interface experiment of control of a movement of a physical object, a robot.[20][21] It was the first experiment to demonstrate control of a physical object, a robot, using EEG.[22][23]

See also[edit]

Brain waves[edit]

References[edit]

  1. ^Foster, JJ; Sutterer, DW; Serences, JT; Vogel, EK; Awh, E (July 2017). "Alpha-Band Oscillations Enable Spatially and Temporally Resolved Tracking of Covert Spatial Attention". Psychological Science. 28 (7): 929–941. doi:10.1177/0956797617699167. PMC 5675530. PMID 28537480.
  2. ^İnce, Rümeysa; Adanır, Saliha Seda; Sevmez, Fatma (2020-03-05). "The inventor of electroencephalography (EEG): Hans Berger (1873–1941)". Child's Nervous System. doi:10.1007/s00381-020-04564-z. ISSN 1433-0350.
  3. ^Berger, Hans (1929-12-01). "Über das Elektrenkephalogramm des Menschen". Archiv für Psychiatrie und Nervenkrankheiten (in German). 87 (1): 527–570. doi:10.1007/BF01797193. ISSN 1433-8491.
  4. ^Palva S.; Palva J.M. (2007). "New vistas for a-frequency band oscillations". Trends Neurosci. 30 (4): 150–158. doi:10.1016/j.tins.2007.02.001. PMID 17307258. S2CID 9156592.
  5. ^Brancaccio, Arianna; Tabarelli, Davide; Bigica, Marco; Baldauf, Daniel (2020-04-24). "Cortical source localization of sleep-stage specific oscillatory activity". Scientific Reports. 10 (1): 6976. doi:10.1038/s41598-020-63933-5. ISSN 2045-2322. PMC 7181624. PMID 32332806.
  6. ^Bagherzadeh, Yasaman; Baldauf, Daniel; Pantazis, Dimitrios; Desimone, Robert (February 2020). "Alpha Synchrony and the Neurofeedback Control of Spatial Attention". Neuron. 105 (3): 577–587.e5. doi:10.1016/j.neuron.2019.11.001. ISSN 0896-6273.
  7. ^Vries, Ingmar E. J. de; Marinato, Giorgio; Baldauf, Daniel (2021-08-24). "Decoding object-based auditory attention from source-reconstructed MEG alpha oscillations". Journal of Neuroscience. doi:10.1523/JNEUROSCI.0583-21.2021. ISSN 0270-6474. PMID 34429378.
  8. ^Domino E. F.; Ni L. S.; et al. (2009). "Tobacco smoking produces widespread dominant brainwave alpha frequency increases". International Journal of Psychophysiology. 74 (3): 192–198. doi:10.1016/j.ijpsycho.2009.08.011. PMC 2788071. PMID 19765621.
  9. ^Niedermeyer E (1997). "Alpha rhythms as physiological and abnormal phenomena". International Journal of Psychophysiology. 26 (1–3): 31–49. doi:10.1016/s0167-8760(97)00754-x. PMID 9202993.
  10. ^Task Force Allas (1992). "ASDA report on EEG arousals: scoring rules and examples". Sleep. 15 (2): 173–184. doi:10.1093/sleep/15.2.173.
  11. ^Lomas T, Ivtzan I, Fu CH (2015). "A systematic review of the neurophysiology of mindfulness on EEG oscillations". Neuroscience & Biobehavioral Reviews. 57: 401–410. doi:10.1016/j.neubiorev.2015.09.018. PMID 26441373.
  12. ^Cahn BR, Polich J (2006). "Meditation states and traits: EEG, ERP, and neuroimaging studies". Psychological Bulletin. 132 (2): 180–211. doi:10.1037/0033-2909.132.2.180. PMID 16536641.
  13. ^Roizenblatt, S.; Moldofsky, H.; Benedito-Silva, A. A.; Tufik, S. (January 2001). "Alpha sleep characteristics in fibromyalgia". Arthritis and Rheumatism. 44 (1): 222–230. doi:10.1002/1529-0131(200101)44:13.0.CO;2-K. ISSN 0004-3591. PMID 11212164.
  14. ^Manu, Peter; Lane, Thomas J.; Matthews, Dale A.; Castriotta, Richard J.; Watson, Robert K.; Abeles, Micha (1994). "Alpha-delta sleep in patients with a chief complaint of chronic fatigue". Southern Medical Journal. 87 (4): 465–470. doi:10.1097/00007611-199404000-00008. PMID 8153772. S2CID 21961157.
  15. ^"Brain Wave Patterns Can Predict Blunders, New Study Finds". UC Davis News and Information. University of California, Davis campus. 23 March 2009.
  16. ^JELL‐0 TEST FINDS LIFELIKE SIGNAL, By BOYCE RENSBERGER, New York Times, MARCH 6, 1976
  17. ^"Can brainwaves be detected in lime Jell-O?". straightdope.com. 11 June 2010. Retrieved 7 April 2018.
  18. ^Karbowski K (2002). "Hans Berger (1873-194)". Journal of Neurology. 249 (8): 1130–1131. doi:10.1007/s00415-002-0872-4. PMID 12420722. S2CID 32730261.
  19. ^Ulrich Kraft. Train Your Brain-Mental exercises with neurofeedback may ease symptoms of attention-deficit disorder, epilepsy and depression--and even boost cognition in healthy brains. Scientific American. 2006
  20. ^S. Bozinovski, M. Sestakov, L. Bozinovska: Using EEG alpha rhythm to control a mobile robot, In G. Harris, C. Walker (eds.) Proc. IEEE Annual Conference of Medical and Biological Society, p. 1515-1516, New Orleans, 1988
  21. ^S. Bozinovski: Mobile robot trajectory control: From fixed rails to direct bioelectric control, In O. Kaynak (ed.) Proc. IEEE Workshop on Intelligent Motion Control, p. 63-67, Istanbul, 1990
  22. ^M. Lebedev: Augmentation of sensorimotor functions with neural prostheses. Opera Medica and Physiologica. Vol. 2 (3): 211-227, 2016
  23. ^M. Lebedev, M. Nicolelis: Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation, Physiological Review 97:737-867, 2017

Further reading[edit]

  • Brazier, M. A. B. (1970), The Electrical Activity of the Nervous System, London: Pitman, PMID 14208567
Sours: https://en.wikipedia.org/wiki/Alpha_wave
Alpha Waves - Improve Your Memory - Super Intelligence

The wandering mind oscillates: EEG alpha power is enhanced during moments of mind-wandering

Abstract

What is your brain doing while your mind is wandering? This study used a within-subjects experience-sampling design to test whether episodes of mind-wandering during a demanding cognitive task are associated with increases in EEG alpha power. Alpha refers to cyclic oscillations in EEG activity at 8–12 Hz, and has been previously correlated with internally rather than externally directed cognition. Participants completed a speeded performance task with more than 800 trials while EEG was recorded. Intermittent experience-sampling probes asked participants to indicate whether their mind was wandering or on-task. Participants reported mind-wandering in response to approximately half of the probes. EEG alpha power was significantly higher preceding probes to which participants reported mind-wandering, compared with probes to which participants reported being on task. These findings imply that dynamic changes in alpha power may prove a valuable tool in studying momentary fluctuations in mind-wandering.

Mind-wandering is a common occurrence, but it is challenging to study because it is internally experienced and spontaneously generated. Sometimes referred to as daydreaming, task-unrelated thought, or off-task cognition, mind-wandering generally refers to cognition that is directed toward an internal stream of thought rather than toward external stimuli or tasks immediately at hand (Smallwood & Schooler, 2015). A continuing methodological challenge is identifying when mind-wandering episodes occur, as cognition unfolds on a dynamic moment-to-moment basis.

Measures of brain activity could provide indices of mind-wandering, as well as information about its neural basis, if such measures could be reliably associated with mind-wandering episodes (Gruberger, Ben-Simon, Levkovitz, Zangen, & Hendler, 2011). Some fMRI studies have found that activity increases in a set of brain regions known as the default mode network during states associated with mind-wandering, such as conditions when the participant has no explicit task (resting state versus a task-directed condition) or when the participant reports engaging in more versus less spontaneous thought (Andrews-Hanna, Irving, Fox, Spreng, & Christoff, 2018). Although such fMRI approaches are promising, the prohibitive cost and practical constraints of fMRI, combined with a sluggish hemodynamic response that may not easily track momentary changes leave the door open for complementary approaches.

The temporal precision of EEG makes it a promising technique for studying mind-wandering episodes. Specifically, EEG alpha activity may provide a potential indirect index of internally directed thought. Since the invention of EEG technology in the 1920s, alpha waves, which are patterns of oscillatory EEG activity cycling at 8–12 Hz, have been associated with mental states conducive to mind-wandering. Alpha waves are higher in waking eyes-closed compared with eyes-open conditions (Adrian & Mathews, 1934). During cognitive tasks, alpha waves are suppressed in response to external stimulation, such as task-relevant visual images or warning cues (e.g., Thut, Nietzel, Brandt, & Pascual-Leone, 2006). Conversely, alpha waves are enhanced during brief waiting intervals between task trials (Carp & Compton, 2009; Compton, Arnstein, Freedman, Dainer-Best, & Liss, 2011) and during mental imagery, which requires inward-directed thought (Cooper, Croft, Dominey, Burgess, & Gruzelier, 2003). Increased alpha activity prior to an incoming stimulus is associated with decrements in perceptual discrimination (e.g., van Dijk, Schoffelen, Oostenveld, & Jensen, 2008). Finally, alpha-wave activity has been correlated with default-mode-network activity in joint EEG–fMRI research (Knyazev, Slobodskoj-Plusnin, Bocharov, & Pylkova, 2011; Mo, Liu, Huang, & Ding, 2013). Together, these results provide a circumstantial argument that alpha oscillations and mind-wandering may co-occur.

Several prior studies have more directly examined the relationship between mind-wandering and EEG alpha power, with somewhat conflicting results. In one of the first studies to examine this relationship, Braboszcz and Delorme (2011) asked participants to count their own breaths and to press a button when their minds wandered away from this primary task. Contrary to the expectation that alpha power would be positively correlated with mind-wandering, these researchers found the opposite: alpha power was decreased in a window of time prior to a button-press indicating mind-wandering. However, two factors limit interpretation of this study. First, the primary task, breath-counting, involves internally directed attention, which would be expected to increase alpha activity under the assumptions that alpha taps internally versus externally driven attentional focus. Second, the study used a “self-caught” method of identifying episodes of mind-wandering, in which the event that marked mind-wandering was the participant’s meta-awareness that mind-wandering had occurred. Conscious awareness of one’s own mind-wandering may tap different mechanisms than mind-wandering itself. For both of these reasons, the findings of Braboszcz and Delorme (2011) may not characterize the neural processes involved when a participant’s attention drifts away from an external task and toward an internal train of thought.

Other studies have found, in contrast to Braboszcz and Delorme (2011), evidence that associates increased EEG alpha power with mind-wandering episodes. For example, prestimulus alpha power during a challenging perceptual discrimination task was positively correlated with self-reports of unfocused attention (Macdonald, Mathan, & Yeung, 2011). Likewise, in a repetitive driving simulation study carried out over a period of 5 days, episodes of mind-wandering (identified through responses to unpredictable experience-sampling probes) were characterized by increased alpha power (Baldwin et al., 2017). On the other hand, in a study using experience-sampling probes of mind-wandering during a reading comprehension task, prestimulus alpha was not associated with self-reports of mind-wandering (Broadway, Franklin, & Schooler, 2015). Together, these mixed findings imply a need for additional study examining the relationship between alpha and mind-wandering across different task contexts.

The present study sought to test the association between alpha oscillations and mind-wandering during a commonly used task of attention and cognitive control—the Stroop task. Experience-sampling probes, querying whether the participant was mind-wandering just prior to the probe, were occasionally interspersed during a single session of the task involving hundreds of trials. Using a within-subjects design, we contrasted EEG activity in windows of time preceding the probes as a function of whether the participant indicated mind-wandering in response to the probe. This allowed us to test the hypothesis that alpha activity is higher during periods of mind-wandering compared with periods of on-task cognition.

Method

Participants

Participants were 62 undergraduates (33 female, 25 male, four other/unreported gender) prescreened to exclude those who were color-blind or nonnative English speakers. All participants gave written informed consent, and the procedures were approved by the Haverford College Institutional Review Board.

The data were collected as part of a more comprehensive study examining neural aspects of error-related cognitive control in the Stroop task. The primary results of the study pertaining to error-related EEG patterns, associated pupil diameter, and performance are reported elsewhere (Compton et al., 2019). That separate report does not address any questions about mind-wandering, which is the focus of this report.

Task

Participants completed a six-choice Stroop task, controlled by E-Prime software. The task was to identify the font color (red, orange, yellow, green, blue, purple) of each stimulus word while ignoring the word meaning. Responses were made via key press using the first three fingers of each hand in normal typing position. One-third of the trials were congruent (word and color match), one-third were incongruent (word and color mismatch), and one-third were neutral (e.g., word “mouse” in red font).

On each trial, the stimulus word was presented for 150 ms, followed by a blank screen until the participant’s key press, and then by a 1,280-ms blank-screen intertrial interval. The task included 864 Stroop trials, divided into 12 blocks of 72 randomly ordered trials each, with brief self-paced breaks between blocks. The 12 experimental blocks were preceded by 12 practice trials that gave trial-wise feedback to ensure that participants learned the color-response mapping. The entire task lasted approximately 30 minutes.

Experience-sampling probes were placed randomly within each half-block of the task, for a total of 24 probes (two per block). The probe screen asked the participant to indicate with a key press whether, just prior to the probe, the participant was on task, mind-wandering, or a mix of both. On-screen instructions prior to the experimental blocks defined on task as meaning that “you were focused on the task and not thinking about anything unrelated to the task.” Mind-wandering was defined as meaning “you were thinking about something completely unrelated to the task,” such as “what to eat for dinner” “plans with friends,” or “thoughts about an upcoming test” (adapted from Seli, Cheyne, & Smilek, 2013). Both was defined as “you were thinking about two things at once: BOTH the task and something else unrelated to the task. For example, perhaps you were devoting enough of your attention to the task to complete it, but you had ‘spare resources’ to also think about something else entirely at the same time. We will refer to these situations as BOTH to refer to a mix of being partly on task and partly mind-wandering.” The probe response option of Both was included to allow for the possibility that participants judge themselves to be sufficiently attentive to the task and yet still mind-wandering, a state that can occur when a task becomes more automated. After the participant selected one of these options via a key press in response to the probe, a 1,280-ms blank screen intervened before the next stimulus onset.

EEG data acquisition

EEG was recorded using a NuAmps amplifier controlled by Curry software. The following scalp sites, using a QuikCaps elastic cap and Ag/AgCl electrodes, were sampled at 1000 Hz: F3, Fz, F4, FCz, C3, Cz, C4, P3, Pz, P4, PO1, PO2, in addition to eye leads (at the left and right temples and above and below the left eye) and left and right mastoids. The right mastoid was used as the reference at the time of recording. Off-line, EEG data were filtered with a 1–30 Hz bandpass filter and rereferenced to the average of the left and right mastoids.

Because experience-sampling probe onsets and probe responses were marked in the continuous EEG data file, we could identify windows of time preceding probes in which the response to the probe was either on task (OT), mind-wandering (MW), or Both. For the primary analysis, the continuous file was segmented into 5-sec epochs preceding each probe. Because mind-wandering is spontaneous in both its onset and offset, it has no fixed duration nor fixed temporal spacing in relation to an unexpected probe onset. Thus, 5 seconds was chosen arbitrarily as a preprobe time window that seemed likely to capture the momentary nature of mind-wandering while still allowing sufficient EEG data for robust analysis. Subsequently, at the suggestion of peer reviewers, additional analyses examined preprobe epochs of 2 and 10 seconds, as well as a 5-sec epoch spanning from 5 to 10 seconds before the probe, to further explore the limits of the association between preprobe alpha power and the participant’s response to the probe.

EEG power spectra were calculated using the fast Fourier transform for each preprobe epoch, and power spectra were averaged separately for epochs corresponding to each probe-response category. Mean log alpha power (defined as 8–12 Hz) was then extracted for each participant and probe-response category.

Self-report measures

Immediately following the Stroop task, the participant completed a series of self-report questionnaires via an online survey. Participants used a sliding scale to estimate the percentage of the time during the task in which their mind was wandering. Next, the participant answered the five items of the Mind-Wandering Questionnaire (MWQ; Mrazek, Phillips, Franklin, Broadway, & Schooler, 2013), which is intended to tap dispositional tendencies toward task-unrelated thought. As sample item is: “While reading, I find I haven’t been thinking about the text and must therefore read it again.” The MWQ uses a 6-point Likert-type scale, with response options ranging from almost never to almost always. Participants subsequently completed three additional questionnaires that are not further examined here (Positive and Negative Affect Schedule: Watson, Clark, & Tellegen, 1988; Five Facet Mindfulness Questionnaire: Baer et al., 2008; Mood and Anxiety Symptom Questionnaire: Wardenaar et al., 2010).

Results

Probe response data

As described in the Method section, participants categorized their experience (OT, MW, or Both) in response to each of 24 experience-sampling probes. The mean number of responses (counts) in each of these categories was M = 10.8 for OT, M = 5.7 for MW, and M = 7.5 for Both. Individual participants varied widely in their counts in each category (range across participants, 0–23 for OT responses, 0–24 for MW responses, and 0–16 for Both responses). As a result, it was not possible to compare EEG data preceding each of the three probe response categories for all participants, because some participants had no instances of a particular category. To maximize the number of participants who could be included in EEG analyses while still maintaining meaningful categories, we summed probe counts across the MW and Both categories to create a combined category of responses (hereafter referred to as MWB), in which any degree of mind-wandering was reported. We then included for subsequent EEG analysis only those participants who had at least five instances of OT responses and five instances of MWB responses. This selection procedure resulted in 50 participants in whom the OT–MWB contrast could be carried out within subjects. For this subsample, the mean number of probe responses was M = 11.5 for OT responses and M = 12.5 for MWB responses. (An alternative participant-selection procedure that required at least seven trials of each type yielded nearly identical statistical outcomes in the analysis of EEG data from the 39 participants who would be included under those stricter criteria.)

Performance data

Mean accuracy on the Stroop task was 91.7% correct. Typical Stroop interference effects were evident in accuracy, F(2, 122) = 12.02, p < .001 (congruent M = 93.0%, SEM = 0.7%, incongruent M = 88.6%, SEM = 1.6%, neutral M = 93.5%, SEM = 0.7%) and in reaction time on correct trials, F(2, 122) = 143.79, p < .001 (congruent M = 615 ms, SEM = 18 ms, incongruent M = 698 ms, SEM = 19 ms, neutral M = 597 ms, SEM = 17 ms).

Stroop-task performance in the two trials preceding probes was examined as a function of probe response category to examine whether episodes of mind-wandering were associated with decrements in performance. A two-trial preprobe window was selected because it approximately matches the 5-second preprobe window of EEG data. This analysis was conducted on the 50 participants meeting criteria of at least five OT and five MWB responses. Accuracy was slightly but nonsignificantly lower in the two Stroop trials preceding MWB probe responses (M = 88.0%, SEM = 1.9%) compared with OT probe responses (M = 90.4%, SEM = 1.4%), t(49) = 1.46, p = .152, d = .206. Likewise, responses were nonsignificantly slower on the two Stroop trials preceding MWB probe responses (M = 656 ms, SEM = 24 ms) compared with OT probe responses (M = 617 ms, SEM = 22 ms), t(49) = −1.90, p = .064, d = .268. Comparisons of preprobe behavior yielded similar conclusions when only one trial prior to the probe was considered (accuracy: pre-MWB M = 87.4%, pre-OT M = 89.7%), t(49) = 1.06, p > .29 (reaction time: pre-MWB M = 663 ms, pre-OT M = 613 ms), t(49) = −1.77, p < .09. Restricting analyses to behavior preceding an OT or MW response, rather than comparing OT with combined MWB responses, did not yield significant preprobe performance differences (ps > .40; restricted to n = 24 participants with at least five OT and at least five MW probe responses).

Retrospective self-report data

We examined whether retrospective self-report measures of mind-wandering were correlated with experience-sampling probe responses. For two participants, retrospective data were unavailable due to experimenter error. For the remaining 60 participants, the retrospective estimate of time spent mind-wandering during the Stroop task (M = 45.9%, SD = 19.9%) was negatively correlated with the OT probe response count (r = −0.70, p < .001), positively correlated with the MW probe response count (r = .66, p < .001 ), and marginally correlated with the Both probe response count (r = .23, p < .09). Thus, participants’ retrospective estimate immediately following the task tended to match their responses to the experience sampling probes during the task. In contrast, scores on the 5-item MWQ, which is intended to tap trait-like aspects of mind-wandering, were not significantly correlated with either the probe response counts nor the retrospective estimate of mind-wandering during the task (ps > .15).

EEG data

The next step in the analyses addressed the main hypothesis by comparing EEG alpha power values for the 5-second interval just prior to probes as a function of the participant’s response to the probe. Log alpha power values were submitted to a 2 × 12 repeated-measures ANOVA, with probe response type (OT, MWB) and electrode site as factors. The main effect of probe response type, F(1, 49) = 20.98, p < .001, ηp2 = 0.30, was due to higher alpha power preceding probes that elicited MWB responses (M = 0.97 μV2, SEM = 0.05) compared with probes that elicited OT responses (M = 0.91 μV2, SEM = 0.05). The main effect of site, F(11, 539) = 14.02, p < .001, ηp2 = 0.22, and the Probe Response Type × Site interaction, F(11, 539) = 5.51, p < .005, ηp2 = .10, generally reflected higher alpha power toward posterior sections of the scalp, and a greater OT–MWB probe response differentiation toward posterior sections of the scalp. Fig. 1 shows means for the interaction.

EEG alpha power during the 5-second window preceding experience-sampling probes, separately for probes to which participants indicated mind-wandering versus on-task cognition. Results are displayed separately for 12 electrode sites represented schematically on the scalp

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While the primary analysis focused on alpha power in a 5-second preprobe epoch, additional analyses were conducted on 2-second and 10-second preprobe epochs on an exploratory basis. For each analysis, a 2 × 12 (Probe Response Type × Electrode Site) repeated-measures ANOVA was conducted on alpha power. The main effect of probe response type was significant when the preprobe epoch was 2 seconds, F(1, 49) = 11.68, p < .001, ηp2 = 0.19 (pre-MWB M = 2.46 μV2, pre-OT M = 2.30 μV2) and when the preprobe epoch was 10 seconds, F(1, 49) = 40.56, p < .001, ηp2 = 0.45 (pre-MWB M = 0.66 μV2, pre-OT M = 0.61μV2). Thus, across each of the three epochs lengths (2, 5, and 10 seconds before the probe), preprobe alpha power was significantly higher preceding a MWB compared with an OT response to the probe. Furthermore, analysis on data extracted from the epoch from −5 to −10 seconds in relation to the probe found again a main effect of probe response type, F(1, 49) = 33.44, p < .001, ηp2 = 0.41, again with higher alpha preceding a MWB response (M = 0.97 μV2) compared with an OT response (M = 0.90 μV2). Direct comparison of data from the 0-s to −5-s preprobe epoch versus the −5-s to −10-s preprobe epoch, in an ANOVA with epoch, probe response type, and site found again a main effect of probe response type, F(1, 49) = 39.17, p < .001, ηp2 = 0.44, that did not further interact with epoch (F < 1).

Although statistical analyses focused on the alpha frequency band, for which prior literature supports a rationale for hypothesizing effects related to mind-wandering, Fig. 2 presents the power spectrum for all measured EEG frequencies at a representative site (Pz). As evident in the Fig. 2, the 8–12 Hz alpha frequency band distinguishes between on-task and mind-wandering episodes while other frequencies generally do not.

EEG power (in μV2) across all measured frequencies in the 5-second window prior to experience-sampling probes. Data are represented separately for probes to which participants indicated some degree of mind-wandering versus being fully on task. Data are taken from the Pz electrode site. Mind-wandering versus on-task episodes are best distinguished by frequencies in the alpha range (8–12 Hz)

Full size image

Exploratory correlational analyses

Exploratory analyses examined correlations among variables measured in the preprobe window, to address whether participants with greater alpha differentiation between MWB and OT responses would also show greater performance differentiation prior to the probes. First, for alpha power data, we subtracted alpha power in the 5-s window preceding OT probe responses from alpha power preceding MWB probe responses. This subtraction creates a probe–response difference score in which a higher score means relatively more alpha preceding MWB probe responses compared with OT probe responses. This alpha probe–response difference score was correlated with performance-based differences scores. We computed two behavioral indices: Accuracy 2 trials previous to an OT probe response minus Accuracy 2 trials previous to a MWB probe responses (accuracy probe–response difference score), and Reaction Time 2 trials previous to MWB probe responses minus Reaction Time 2 trials previous to an OT probe response (reaction-time probe–response difference score).

The alpha-based probe–response difference score was not significantly related to the reaction-time probe–response difference score, but unexpectedly it was inversely related to the accuracy probe–response difference score (r = −0.327, p < .05). This inverse relationship remained significant even when mean accuracy and mean alpha power were statistically controlled in a partial correlation (partial r = −.341, p < .02, df = 46). In sum, somewhat surprisingly, the participants whose alpha power in the preprobe epoch best differentiated between MWB versus OT probe responses were those whose accuracy was least detrimentally affected by MWB versus OT probe responses.

Discussion

This study combined experience-sampling probes and EEG recording to identify neural signatures associated with mind-wandering. During a repetitive and lengthy Stroop task, participants indicated some degree of mind-wandering in response to about half of the randomly placed experience sampling probes, and they retrospectively estimated that they spent an average of 46% of their task time mind-wandering. The key finding of this study was that in a 5-s window of time preceding an experience sampling probe, EEG alpha power was higher if participants indicated some degree of mind-wandering instead of being fully on task prior to the probe. The same pattern of results was obtained when the preprobe epoch was shortened to 2 seconds or increased to 10 seconds. Furthermore, EEG data within an epoch 5 to 10 seconds before the probe was equally predictive of a mind-wandering response as data within an epoch 0 to 5 seconds before the probe. Although task parameters precluded analyzing even longer preprobe epochs, future studies could further explore how long before a self-report of mind-wandering the increased alpha patterns are evident.

The results provide strong evidence of an association between mind-wandering and EEG alpha activity, consistent with prior theory and research associating alpha oscillations with internally directed cognition (e.g., Adrian & Mathews, 1934; Thut et al., 2006). As such, the results complement other findings of reduced neural indices of attention toward incoming stimuli during episodes of mind-wandering (Barron, Riby, Greer, & Smallwood, 2011; Handy & Kam, 2015; Smallwood, Beach, Schooler, & Handy, 2008). That is, the present data imply that mind-wandering is associated with increases in neural measures assumed to reflect inward-directed cognition, while prior studies imply that it is also associated with decreases in neural measures assumed to reflect externally driven cognition. Recent work found that a machine learning algorithm that combines oscillatory EEG measures such as alpha power with ERP indices of externally driven attention was able to predict the presence of a mind-wandering state at above-chance levels, with alpha power serving as the most reliable individual predictor (Jin, Borst, & van Vugt, 2019).

These results are consistent with prior findings associating higher alpha power with mind-wandering or attentional lapses (Baldwin et al., 2017; Macdonald et al., 2011), while standing in contrast to studies that found the opposite pattern (Braboszcz & Delorme, 2011) or no difference in alpha power depending on self-reported mind-wandering (Broadway et al., 2015). Given differences in methodology among these studies, the critical variables contributing to different outcomes have yet to be fully identified. However, because the only study to directly contradict the current findings is one that used a primary task of internally directed attention (breath-counting; Braboszcz & Delorme, 2011), it seems likely that alpha power provides a better index of off-task thought when the primary task involves responding to external stimuli, as in the present study and others that produced similar results (Baldwin et al., 2017; McDonald et al., 2011).

Although Stroop performance was also slightly worse preceding mind-wandering versus on-task probe responses, performance measures did not distinguish between mind-wandering versus on-task experience as robustly as EEG alpha power did. One possibility is that this EEG-versus-performance difference is due to measurement issues: EEG data are more fine-grained, with 5,000 data points per electrode during a 5-second interval, as opposed to two button presses within that same approximate time interval. Thus, perhaps performance measures are subject to more random noise and therefore less able to demonstrate effects of the mental state of mind-wandering.

A second, and possibly more interesting, possibility is that EEG measures provide a more direct window onto mind-wandering than do performance measures, reflecting the process of mind-wandering itself rather than the effects of that process on other aspects of cognition. As a task becomes more practiced and automated, its demand on cognitive resources decreases, and participants may be able to sustain sufficient levels of performance while still allowing their minds to wander (Thomson, Besner, & Smilek, 2013). Indeed, in the present study, participants maintained a high accuracy rate (92% correct) across more than 800 trials of a challenging six-choice Stroop task even while self-reporting that their minds wandered almost half the time. Particularly for overpracticed tasks or for participants with high cognitive capacity, EEG measures may index a mind-wandering attentional state that is not as easily discernible with overt performance measures alone. This interpretation is supported by the intriguing yet unexpected finding that participants whose alpha power best distinguished self-reported mind-wandering from on-task attentional states were those whose performance accuracy was least disrupted by mind-wandering.

While the robust results imply a close connection between alpha oscillations and mind-wandering experiences, they leave the causal nature of the relationship open to future research. Additional studies could systematically vary task conditions known to elicit mind-wandering (such as motivation or cognitive load) and examine how alpha power changes as a result. Conversely, researchers have used 10-Hz rhythmic transcranial magnetic stimulation of the brain to cause perceptual changes previously correlated with alpha rhythms (Romei, Gross, & Thut, 2010; Thut, Schyns, & Gross, 2011). Using a similar method, future studies could test whether stimulation in the alpha frequency band can elicit episodes of mind-wandering (cf. Clayton, Yeung, & Cohen Kadosh, 2019). In addition, while the present study focused primarily on alpha rhythms, because of the robust prior literature motivating hypotheses about this measure, other studies may choose to expand beyond alpha to examine other oscillatory EEG phenomena (e.g., van Son et al., 2019).

Future studies may also examine the relationships among pupil dilation, EEG alpha power, and mind-wandering. Prior research has found that pupil diameter covaries with mind-wandering states (e.g., Unsworth & Robison, 2016, 2017; see also Mittner et al., 2014), possibly reflecting a role of the norepinephrinergic system in attentional fluctuations (Mittner, Hawkins, Boekel, & Forstmann, 2016). Although the present study collected pupillary measurements, those measurements were only obtained for the Stroop trials in the present study, not the probe response trials, because the aim was to examine pupillary reactions following Stroop performance errors (Compton et al., 2019). Thus, associations between pupil dilation and mind-wandering could not be directly examined within the present data set. However, other findings indicate that EEG alpha power and pupil dilation are inversely related (Compton et al., 2019), possibly reflecting a common arousal system and opening possibilities for further investigation of joint pupillary–EEG predictions of mind-wandering states.

While the main results of the study are statistically robust and support the hypothesis that internally directed thought is characterized by enhanced alpha rhythms, one limitation of the study involves the nature of the mind-wandering probes. It is challenging to define and measure a mental state that is, by definition, spontaneous and internally experienced (Smallwood & Schooler, 2015). While randomly placed experience-sampling probes have advantages as a method of evaluating the presence of mind-wandering, their usefulness is limited by the response options available as well as by participants’ ability to introspect about the states being probed. In the present study, participants were given three response choices (on task, mind-wandering, or both), which may not sufficiently differentiate among related, but distinct mental states. For example, some prior findings have distinguished between intentional and unintentional mind-wandering (Seli, Risko, & Smilek, 2016; Seli, Risko, Smilek, & Schacter, 2016), and others have differentiated among states of task-related interference, mind-wandering, and external distraction (e.g., Stawarczyk, Majerus, Maquet, & D’Argembeau, 2011). In the present study, the response option of “both” was intended to capture states in which participants divided attention between the primary task and internally generated thoughts; however, because of the uneven distribution of responses across the three probe response options, it was necessary to merge the “both” responses with the mind-wandering responses to achieve sufficient trials for analysis. Future research would benefit from considering additional ways to parse the concept of mind-wandering and related experiences, which could in turn lead to more specific interpretations of the role of alpha rhythms in supporting experiences of internally directed thought.

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Acknowledgements

Paige Carson, Elizabeth Heaton, Stephanie Histon, Taylor Levine, and Danielle Rette assisted with data collection. This study was supported by NSF RUI Grant No. 1632584.

Open practices statement

The data from this study are available from the first author upon request. The study was not preregistered.

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  1. Department of Psychology, Haverford College, 370 Lancaster Ave, Haverford, PA, 19041, USA

    Rebecca J. Compton, Dylan Gearinger & Hannah Wild

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Correspondence to Rebecca J. Compton.

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Compton, R.J., Gearinger, D. & Wild, H. The wandering mind oscillates: EEG alpha power is enhanced during moments of mind-wandering. Cogn Affect Behav Neurosci19, 1184–1191 (2019). https://doi.org/10.3758/s13415-019-00745-9

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Keywords

  • Mind-wandering
  • EEG
  • Alpha oscillations
  • Experience sampling
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Power eeg alpha

Regional electroencephalogram (EEG) alpha power and asymmetry in older adults: a study of short-term test–retest reliability

Introduction

During relaxed wakefulness, the human electroencephalogram (EEG) is dominated by oscillations in the alpha frequency band (∼7.5–12.5 Hz). Resting alpha activity is reported to be unique to the individual (Benz et al., 2013), heritable (Smit et al., 2005; Anokhin et al., 2006), and stable (Salinsky et al., 1991). In certain conditions, however, individual differences in resting alpha activity reflect internal changes such as increasing fatigue (Simon et al., 2011), or reduced anxiety (Boutcher and Landers, 1988; Crabbe and Dishman, 2004), for instance, following the performance of demanding cognitive or physical tasks. Although these overall patterns are well-documented in younger adults, relatively little is known about alpha activity among healthy older adults.

Extant reports suggest that resting alpha activity is lower in older than younger adults (Sander et al., 2012), and further reduced in the presence of cognitive impairment (Koenig et al., 2005). Furthermore, the ability to modulate alpha power does not come easily to older adults (e.g., suppressing the processing of irrelevant information, Vaden et al., 2012; cf. Payne et al., 2013), and tends to break down readily under high-load conditions (Sander et al., 2012). Resting alpha activity appears to be both vulnerable to increased age and sensitive to the demands of effortful cognitive processing and physical activity.

Other studies have used the pattern of resting frontal EEG alpha asymmetry to understand dispositional mood and affective processing (e.g., Sutton and Davidson, 1997; Coan and Allen, 2004). For example, relatively greater frontal activity in the left hemisphere has been associated with behavioral approach and positive affect, whereas greater right-sided activity has been associated with behavioral inhibition and negative affect (Davidson, 1992, 2000; Sutton and Davidson, 1997). Frontal EEG asymmetry at rest has also been characterized as a diathesis that may be modified by salient stimuli of sufficient intensity (Petruzzello et al., 2001). For example, frontal alpha asymmetry is susceptible to procedures such as emotion induction via the use of emotional film clips (e.g., Wheeler et al., 1993) or specific training (e.g., Davidson et al., 2003), in younger adults. Frontal asymmetry also appears to be responsive to interventions such as mindfulness mediation training (Davidson et al., 2003), and cognitive behavior therapy (CBT; Moscovitch et al., 2011), which have been reported to sustain (Moynihan et al., 2013) or even increase left frontal asymmetry (Davidson et al., 2003).

Although studied far less frequently in older adults, relatively greater left frontal activity in this age group has been associated with facets of well-being, including life-satisfaction, autonomy, and engagement (Urry et al., 2004). In younger adults, such elements of well-being have been related to behavioral approach tendencies, sociability, and positive affect (e.g., Schmidt, 1999).

Although acceptable test–retest reliability of these measures has been demonstrated across different contexts (Schmidt et al., 2003), in younger adults (Tomarken et al., 1992; McEvoy et al., 2000; Winegust et al., 2014), children (Vuga et al., 2008), and in some clinical populations (e.g., Allen et al., 2004; Vuga et al., 2006; Schmidt et al., 2012), relatively few studies have examined short-term test–retest reliability of regional EEG alpha power and asymmetry measures in older individuals. If relatively greater left frontal asymmetry at rest is a reliable measure of psychological (Urry et al., 2004) and physiological (Davidson et al., 2003) well-being in older adults, then these metrics should show acceptable levels of test–retest reliability within the individuals tested. Examining the test–retest reliability of alpha measures is a first step in ensuring their psychometric soundness in older adults.

The Present Study

Here, we assessed the short-term test–retest reliability of resting regional EEG alpha power and asymmetry measures in a community-dwelling sample of older adults, with particular attention to brain activity in the frontal regions. We examined resting regional alpha power and relative asymmetry before and after a challenging perceptual task. At the level of individual differences, moderate to strong correlations were expected between pre- and post-task resting conditions for both alpha power and asymmetry. Given that resting frontal asymmetry is described as dispositional (in the absence of intentional mood induction or interventions), asymmetry in frontal regions was expected to be correlated (i.e., reliable) between pre- and post-task conditions. Resting alpha power was expected to be more easily altered by the intervening task. As alpha power may increase due to fatigue, relaxation, or reduced anxiety following completion of a challenging task (Crabbe and Dishman, 2004), we anticipated that post-task levels of resting alpha power would be higher than pre-task levels.

Materials and Methods

Participants

Forty-one (20 females) older adults (M = 71.5 years, SD = 6.5 years, range: 61–86 years) were tested in the Vision and Cognitive Neuroscience Laboratory at McMaster University. All participants reported normal health, being free of neurological or psychiatric disorders, and living in the local community. Data from three participants were excluded because they did not have sufficient segments in either the pre- or post-task EEG recordings (<40, Towers and Allen, 2009), leaving data from 38 participants available for analysis. Sample characteristics are presented in Table 1.

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TABLE 1.Sample characteristics.

Procedures

Participants were introduced to the laboratory at McMaster University and briefed about the study procedures. Informed consent was obtained prior to testing. Throughout the testing session, participants were seated in a comfortable chair in a dimly-lit, copper-shielded room maintained at a comfortable temperature. Regional EEG data were continuously recorded during seated rest, prior to performance of a visual perception task (T1), and immediately following the task (T2), as part of a larger ERP study. Following the EEG testing, resting blood pressure and a brief cardiac recording were taken, after which participants completed several questionnaires for use in the larger study. Upon completion of testing, participants were debriefed and given a nominal reimbursement for their time and travel expenses. The study received clearance from the McMaster Research Ethics Board.

Regional EEG Data Collection and Reduction

EEG Recording

Resting EEG data were recorded continuously using a 256-channel HydroCel Geodesic Sensor Net [Electrical Geodesics, Inc., Eugene, OR, USA (EGI)] during a 6-min baseline before and after the visual perceptual task, alternating 1-min intervals between eyes-closed (EC) and eyes-open (EO) conditions. During acquisition, impedances were kept below 50 kΩ, in accord with recommendations in the Electrical Geodesics, Inc. (2006) published by EGI. EEG signals were sampled at 250 Hz, referenced to the vertex (Cz), digitized with a 16-bit analog-to-digital converter (ADC), and hardware-filtered using an analog filter from 0.01 to 100 Hz. Participants were instructed to relax and minimize movements.

EEG Data Reduction

Offline, any channel with consistent artifact was interpolated from the channel’s nearest neighboring sites prior to further analysis using Brain Vision Analyzer 2.0.4 (Brain Products, GmbH, Gilching, Germany). EEG data were band-pass filtered between 0.1 and 50 Hz, the sampling rate was changed from 250 to 256 Hz, noisy channels were interpolated, and the data were edited for artifacts, using a ±200 μV criterion. If artifacts were present in one channel, data in all channels were excluded for that epoch. Artifact-free EEG data from the EC and EO conditions were analyzed separately using a fast Fourier transform (FFT), with a Hanning window of 1-s width with 10% overlap of epochs. EEG power was derived for the traditional frequency bands: delta, 0.5–3.5 Hz; theta, 3.5–7.5 Hz; alpha, 7.5–12.5 Hz; beta 12.5–30 Hz; and gamma, 30–50 Hz. Given previously reported associations between frontal alpha asymmetry and affective processing, power in the alpha band was of particular interest. For each 1-min interval, EEG data were analyzed beginning 5-s after the instruction to open or close the eyes. Estimates of EEG power were based on an average of 229 (SD = 62) 1-s epochs, with a minimum of 56 epochs, and averaged within the EC or EO conditions separately.

EEG Alpha Measures

Regional Alpha Power

Electroencephalogram rhythms may be measured in terms of power (μV2) or its square root, amplitude (μV). EEG power was derived for all frequency bands using amplitude (μV). However, we refer to “alpha power” from this point forward, as this term is more commonly understood. EEG clusters of electrode channels were identified as corresponding to each of the relevant sites from the International 10/20 Electrode Placement System (Jasper, 1958). EEG signals for each condition (pre- vs. post-task; EC vs. EO) were averaged separately to form local power values in the left hemisphere for Fp1 (sites 27, 32, 33, 34, 37), F3 (sites 36, 40, 41, 42, 49, 50), F7 (39, 46, 47, 48, 54), C3 (sites 51, 52, 58, 59, 60, 65, 66), T3 (sites 63, 68, 69, 70, 74), P3 (sites 76, 77, 85, 86, 87, 97, 98), P5 (sites 84, 94, 95, 96, 105), O1 (sites 116, 123, 124, 125, 136), and eight homologous sites in the right hemisphere (Fp2, F4, F8, C4, T4, P4, P6, and O2). EEG power values for each (clustered) site were natural-log (ln) transformed to normalize the data, and reported separately for the EC and EO conditions.

Regional Alpha Asymmetry

Eight measures of regional alpha asymmetry (prefrontal, mid-frontal, lateral frontal, central, temporal, parieto-temporal, parietal, occipital regions) were calculated separately by subtracting natural-log transformed regional EEG alpha power in the left hemisphere from natural-log transformed power at homologous sites in the right hemisphere [e.g., ln(right) power minus ln(left) power], separately for the EC and EO conditions, during pre- and post-task rest. EEG alpha power is inversely related to cortical activity. Therefore, positive values of frontal asymmetry reflect relatively greater alpha power in the right hemisphere, indicating greater activity in the left hemisphere, whereas negative values represent relatively greater alpha power in the left hemisphere, indicating greater activity in the right hemisphere (Davidson, 1992).

Results

Test–Retest Reliability of Regional Alpha Power and Asymmetry Measures Pre- vs. Post-Task

Mean values for pre-task (T1) and post-task (T2) alpha power and asymmetry are presented by region for the EC and EO conditions in Tables 2A,B, respectively.

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TABLE 2.Mean (SD) and test–retest reliability coefficients for left and right resting EEG alpha power (in μV) and regional asymmetry in older adults, before and after task performance for (A) eyes closed and (B) eyes open conditions (n = 38).

Regional Alpha Power

Test–retest reliability between pre-task and post-task alpha power measures was excellent for the EC and EO conditions (EC: ICCs = 0.90–0.97, ps < 0.001; EO: ICCs = 0.84–0.95, ps < 0.001). Importantly, measures of pre- and post-task alpha power were also highly correlated, indicating that individual differences in alpha power evident before the task (and their rank order) were clearly preserved after the task (all ps < 0.001; see Table 2).

Regional Alpha Asymmetry

Test–retest reliability between pre- and post-task regional asymmetry was very good (EC: ICCs = 0.65–0.91, ps < 0.001; EO: ICCs = 0.53–0.86, ps < 0.001). As well, pre- and post-task asymmetry values were highly correlated for both the EC and EO conditions for all regions tested (all ps < 0.001; see Table 2). Individual differences in frontal alpha asymmetry (and their rank order) seen at T1 were well-preserved at T2, after the task (see Figures 1 and 2).

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FIGURE 1.Scatterplots of the associations between mid-frontal asymmetry in the eyes-closed, pre-task (T1), and post-task (T2) conditions.

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FIGURE 2.Scatterplots of the associations between lateral-frontal asymmetry in the eyes-closed, pre-task (T1), and post-task (T2) conditions.

Analysis of Resting EEG Power: Eyes Closed Condition

Eyes-closed pre- and post-task resting EEG activity was analyzed in a 2 × 5 × 2 × 4 omnibus ANOVA, with measurement occasion (pre-task, T1, vs. post-task, T2), frequency (delta, theta, alpha, beta, gamma), hemisphere (left, right), and region (mid-frontal, central, parietal, occipital) as factors. Main effects of measurement occasion, frequency, and region (ps < 0.001) were qualified by two-way interactions. Frequency interacted with measurement occasion, F(4,148) = 3.32, p < 0.03, = 0.08, and region F(12,444) = 16.97, p < 0.001, = 0.31, and the regional effect interacted with hemisphere F(3,111) = 4.04, p < 0.02, = 0.10, with no other effects or interactions, ps > 0.12. Unadjusted pairwise tests indicated that EEG power was greater in the post-task (T2: M = 0.58 μV, SE = 0.09) than pre-task condition (T1: M = 0.35 μV, SE = 0.11; see Figures 3 and 4), and greater at the alpha frequency (M = 1.14 μV, SE = 0.15) than all other frequencies (ps < 0.01), except delta (M = 0.92 μV, SE = 0.09), p > 0.10. EEG power was also greater in mid-frontal (M = 0.66 μV, SE = 0.11) than central (M = 0.31 μV, SE = 0.10), p < 0.001, parietal (M = 0.33 μV, SE = 0.10), p < 0.001, or occipital regions (M = 0.56 μV, SE = 0.10), p < 0.06. In sum, EC resting EEG power differed by frequency, and where and when it was measured, with a pattern that suggested mid-frontal asymmetry in the alpha band frequency.

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FIGURE 3.Eyes-closed EEG power in the left hemisphere, by frequency, region, and condition (T1 vs. T2).

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FIGURE 4.Eyes-closed EEG power in the right hemisphere, by frequency, region, and condition (T1 vs. T2).

Because mid-frontal alpha asymmetry was of particular interest to this study, a 2 × 2 (measurement occasion, hemisphere) ANOVA of alpha power was performed for the mid-frontal region. Mid-frontal alpha power was greater in the post-task (T2: M = 1.20 μV, SE = 0.15) than the pre-task condition (T1: M = 1.00 μV, SE = 0.16), F(1,37) = 14.62, p < 0.001, = 0.28, and significantly greater in the right hemisphere (M = 1.16 μV, SE = 0.15) than the left (M = 1.04 μV, SE = 0.16), F(1,37) = 5.61, p < 0.03, = 0.13, with no interaction, ps > 0.16. Relatively greater frontal alpha power in the right hemisphere reflected greater left frontal asymmetry (i.e., more activity in the left frontal region) in the EC condition.

Analysis of Resting EEG Power: Eyes-Open Condition

Similar to the EC condition, an omnibus 2 × 5 × 2 × 4 ANOVA of EO resting EEG power yielded main effects of measurement occasion, frequency, and region (ps < 0.01), and significant region by frequency, F(12,444) = 23.66, p < 0.001, = 0.39, and region by hemisphere, F(3,111) = 4.53, p < 0.02, = 0.11 interactions. Post-task EO EEG power was higher (T2: M = 0.43 μV, SE = 0.07), than pre-task power (T1: M = 0.25 μV, SE = 0.09; see Figures 5 and 6). Resting EO power was greater in the delta frequency band (M = 1.13 μV, SE = 0.09) than the other frequencies, ps < 0.001, and greater in mid-frontal (M = 0.66 μV, SE = 0.09), than the other regions, ps < 0.001, with no other effects or interactions, ps > 0.13. Like the EC condition, EO resting EEG power differed by frequency, and where and when it was measured, and exhibited a pattern that suggested significant asymmetry in mid-frontal alpha power.

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FIGURE 5.Eyes-open EEG power in the left hemisphere by frequency, region, and condition (T1 vs. T2).

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FIGURE 6.Eyes-open EEG power in the right hemisphere by frequency, region, and condition (T1 vs. T2).

To ascertain whether asymmetry was present in the EO condition, a 2 × 2 ANOVA of EO mid-frontal alpha power was performed, showing that mid-frontal alpha was greater in the post-task (T2: M = 0.50 μV, SE = 0.12) than pre-task condition (T1: M = 0.32 μV, SE = 0.13), F(1,37) = 8.96, p < 0.01, = 0.20, and significantly greater in the right hemisphere (M = 0.48 μV, SE = 0.12) than the left (M = 0.33 μV, SE = 0.13), F(1,37) = 9.52, p < 0.01, = 0.21, with no interaction, p > 0.85. Similarly to the EC condition, relatively greater EO alpha activity in the right hemisphere reflected greater left frontal asymmetry (more activity in the left frontal region), across the group.

Overall, resting EC and EO alpha power increased significantly following task performance in all regions tested, and mid-frontal alpha power was relatively greater in the right hemisphere, reflecting left frontal asymmetry in both EC and EO conditions.

Analysis of Resting Alpha Asymmetry

Alpha asymmetry is most commonly analyzed at frontal (prefrontal, mid-frontal, lateral-frontal), and some posterior (e.g., parietal) sites. Therefore, measures of resting alpha asymmetry from six regions (prefrontal, mid-frontal, lateral-frontal, central, parietal, occipital) were selected and submitted to a 2 × 2 × 6 (EC vs. EO condition by measurement occasion by region) ANOVA. The analysis revealed only a main effect of region, F(5,185) = 3.20, p < 0.04, = 0.08, with no other effects or interactions, p > 0.15. Pairwise tests indicated that mid-frontal asymmetry (M = 0.14 μV, SE = 0.05) was greater than alpha asymmetry at all other sites (all ps < 0.03, except the lateral frontal region (M = 0.08 μV, SE = 0.04), p < 0.07; see Figure 7). The magnitude of parietal asymmetry (M = -0.07 μV, SE = 0.05) did not differ from than that of central (M = -0.04 μV, SE = 0.06), or occipital asymmetry (M = -0.04 μV, SE = 0.04), ps > 0.50, but was significantly lower than that of lateral frontal asymmetry, p < 0.05. To test whether any of the asymmetry values differed significantly from zero, regional asymmetry values at each site were collapsed across the EC and EO conditions and entered in one-sample t-tests. Only mid-frontal asymmetry at T1 and T2, ps < 0.03, and lateral-frontal asymmetry at T2, p < 0.04, differed significantly from zero (all other ps > 0.09).

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FIGURE 7.Overall EEG alpha power in the eyes-closed and eyes-open conditions was greater at T2 than T1, and greater in the frontal right hemisphere than the left.

Predictors of Pre- to Post-Task Increases in Alpha Activity

Given that regional measures of resting alpha power were greatest during post-task rest, an additional set of analyses was performed to ascertain whether the increase was related to individual characteristics, namely, age, sex, education level, handedness, or medication status (taking prescribed medications vs. taking none).

A series of regression analyses was performed on the pre- to post-task change in EC alpha power at each of the 16 sites, with age, sex, education, and handedness as independent predictors. These analyses indicated that sex accounted for significant variance (12–31%) in the post-task increase in alpha power (difference scores) at virtually every site, ps < 0.05, with trends for T3 (p < 0.09, 9%) and O2 (p < 0.06, 11%). Age, education level, handedness, and medication status were non-significant at every site (all ps > 0.20).

Similar results were obtained for EO alpha power. Sex explained significant variance (11–28%) in the post-task increase in alpha power at almost every site, ps < 0.05, with trends for Fp2 (p < 0.06, 10%) and C3 (p < 0.11, 8%). Age, education level, handedness, and medication status did not reach significance at any site (all ps > 0.06) except for the increase at P5, which was positively predicted by age (p < 0.05, 9%; see Table 3).

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TABLE 3.Regression results for sex as a predictor of increased resting EC alpha power (n = 38).

A 2 × 8 (hemisphere × region) ANCOVA of the power difference scores, statistically controlled for sex, indicated that the post-task increase in EC alpha power was larger in women (M = 0.36, SE = 0.06) than men (M = 0.09, SE = 0.06), F(1,36) = 10.19, p < 0.01, = 0.22, and greater in parietal (P3, P4, P5, P6) than frontal regions (Fp1, Fp2, F3, F4, F7, F8), all ps < 0.01 (pairwise), F(7,252) = 4.19, p < 0.01, = 0.10 (see Figure 8). Numerically, the effect size for sex exceeded that of region. There were no interactions, ps > 0.30. The post-task increase in EO alpha power was similar, being larger for women (M = 0.35 μV, SE = 0.07) than men (M = 0.07 μV, SE = 0.07), F(1,36) = 8.49, p < 0.01, = 0.19, and greater in temporo-parietal (T3, T4, P3, P4, P5, P6), relative to frontal regions (Fp1, Fp2, F3, F4, F7, F8), all ps < 0.03 (pairwise), F(7,252) = 6.15, p < 0.001, = 0.15. For the EO condition, sex interacted with hemisphere, with women showing greater right-sided increases in alpha power relative to left-sided increases (R: M = 0.37 μV, SE = 0.07; L: M = 0.33 μV, SE = 0.07), and men showing the reverse (R: M = 0.06 μV, SE = 0.07; L: M = 0.08 μV, SE = 0.07). In contrast to the results with change scores, parallel analyses of simple pre-task (T1) resting alpha power revealed no sex differences for either the EC (p > 0.11) or EO (p > 0.24) conditions. We note, though, that resting alpha power was nominally higher in men (EC: M = 1.33 μV, SE = 0.22; EO: M = 0.54 μV, SE = 0.18) than women (EC: M = 0.84 μV, SE = 0.22; EO: M = 0.24 μV, SE = 0.18) at T1, prior to task performance.

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FIGURE 8.Overall post-task increases in alpha power in the eyes-closed and eyes-open conditions were greater in women than men.

Discussion

Inherent to the notion that resting EEG alpha power and asymmetry are valid measures of dispositional and state processes is the assumption that within-individual differences in these measures remain stable across time and contexts. An initial step in addressing their validity is to confirm their reliability. Here, we sought to establish test–retest reliability in a sample of healthy older adults. We found that individual differences in resting regional EEG alpha power and asymmetry showed good-to-excellent test–retest reliability from pre- to post-task conditions at all sites tested. While similar test–retest reliability has been reported in non-clinical (e.g., Tomarken et al., 1992; McEvoy et al., 2000) and clinical (Schmidt et al., 2012) samples of younger adults, these findings appear to be the first to demonstrate short-term test–retest reliability of regional EEG power and asymmetry in a sample of healthy older adults using a dense array methodology.

The second major finding was that at virtually all of the individual sites tested, resting alpha power increased following performance of a perceptual task, for both the EC and EO conditions. The topography of the increase in alpha power indicated a global change in participants’ electrocortical resting state in response to the intervening task, similar to that reported by Simon et al. (2011) in drowsy drivers. We believe the increase in alpha power following the perceptual task reported here may be related to fatigue, similar to the increase in alpha power that occurs after prolonged driving, another visual perception task demanding attentional control and vigilance. In our sample, post-task alpha power increases were more substantial in older women than in older men, the latter of whom showed more incremental and more variable changes (Figure 8). Because alpha power during pre-task rest was non-significantly higher in men than women, the larger increase in older women served to equalize alpha power differences between sexes in the post-task condition.

The third finding was that because the increase in alpha power from pre- to post-task conditions was global, the balance between alpha power in left and right frontal regions did not change. Consistent with a large literature linking left frontal asymmetry to behavioral approach and positive affect in young adults, resting EEG signals in this sample of healthy older adults exhibited significantly greater resting left frontal than right frontal activation in both EC and EO conditions, at T1 and T2. In addition, the asymmetry pattern was localized primarily to mid-frontal sites, where asymmetry was substantially different from zero.

Although the literature on asymmetry in older adults is scant, our findings are in line with the evidence currently available. Greater left frontal asymmetry has been reported in healthy adults aged 57–60, where it was positively associated with well-being, approach behavior, and agency (Urry et al., 2004). Our findings are also consistent with a study in which left frontal asymmetry was maintained in adults 65 years or older who participated in 8 weeks of mindfulness meditation training, in contrast to a decline in matched wait-list controls over the same period (Moynihan et al., 2013). Increases in left frontal asymmetry, along with positive changes in immune function, have also been reported in young and middle-aged adults who participated in mindfulness mediation training (Davidson et al., 2003), and in socially anxious adults participating in CBT (e.g., Moscovitch et al., 2011).

Overall, our data suggest acceptable short-term test–retest reliability in alpha power and asymmetry at the level of the individual. The present results are in line with the extensive literature on frontal asymmetry in younger adults. These findings also suggest that while mean measures of frontal asymmetry did not change from pre-to post-task conditions, mean levels of alpha power were uniquely sensitive to the experimental task.

Limitations

There are at least two limitations to the interpretation of the findings reported here. First, the findings were derived from a relatively healthy, small sample of community-dwelling older adults, which may limit their generalizability to older adults with significant health problems, and limited mobility, social connections, or economic resources. We note, though, that the adults in our sample spanned a wide range of ages (61–86 years), representing both “young-old” individuals who were still employed or only recently retired, as well as “old–old” adults who were well into their retirement years.

Second, while we have demonstrated sound test–retest reliability with respect to individual differences in resting alpha power and asymmetry in the context of task performance, we have not shown this reliability over an extended period of time. It would be important to establish comparable reliability of alpha power and frontal asymmetry in a longitudinal sample. Yet, we note that post-task increases in resting alpha power have only rarely been reported in the literature, and usually in the context of physical tasks.

Conclusion

Resting frontal alpha power and asymmetry have been linked to stable individual differences in dispositional variables in many previous studies. Simultaneously, they may also demonstrate state-dependent variation in response to changing environmental demands. In the present study, individual differences in resting EEG alpha power and asymmetry from two occasions were highly reliable in a sample of older adults. Although mean levels of alpha power were uniquely sensitive to experimental context (and may have represented greater fatigue in older women than older men), mean measures of frontal alpha asymmetry did not change. Demonstrating within-subject reliability across contexts serves to validate the notion that these measures actually reflect meaningful individual differences that are of potential interest to aging and personality and emotion research. Although studies of EEG alpha power and asymmetry have long been used as psychophysiological measures in younger adults, the results of the present study documenting test–retest reliability of resting frontal EEG alpha power and asymmetry in older adults support the use of these psychophysiological measures in future studies of healthy aging.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This research was supported by NSERC and CIHR operating grants awarded to PB, AS, and LS and a summer NSERC undergraduate award to BS under the direction of LS. The authors would like to thank Donna Waxman for her help with coordinating the visits and data collection.

Footnotes

  1. ^Similar to findings reviewed by Crabbe and Dishman (2004), post-task increases in absolute alpha power were not reflected in analyses of relative alpha power. When EC alpha power was analyzed relative to power in all the other frequencies, a 2 × 2 × 4 (measurement occasion by hemisphere by region) ANOVA revealed that relative EC alpha power showed a marginal decline from pre-task (T1: M = 1.92, SE = 0.84) to post-task (T2: M = 0.35, SE = 0.17), F(1,37) = 3.50, p < 0.07, = 0.09, with no other effects (ps > 0.25). Separate ANOVAs of EC power in the other frequencies indicated that like EC alpha power (p < 0.001, = 0.40), EC theta, beta, and gamma power increased following task performance (ps < 0.001, = 0.29–0.39), in contrast to delta power (p > 0.13, = 0.06), which did not change.

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Keywords: psychophysiology, aging, test–retest reliability, electroencephalogram (EEG), alpha power, frontal asymmetry

Citation: Mathewson KJ, Hashemi A, Sheng B, Sekuler AB, Bennett PJ and Schmidt LA (2015) Regional electroencephalogram (EEG) alpha power and asymmetry in older adults: a study of short-term test–retest reliability. Front. Aging Neurosci. 7:177. doi: 10.3389/fnagi.2015.00177

Received: 12 May 2015; Accepted: 31 August 2015;
Published: 16 September 2015.

Copyright © 2015 Mathewson, Hashemi, Sheng, Sekuler, Bennett and Schmidt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Karen J. Mathewson, Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON L8S 4K1, Canada, [email protected]a

Sours: https://www.frontiersin.org/articles/10.3389/fnagi.2015.00177/full
Electroencephalogram (EEG) - Waves - Physiology

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