Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry

Ezra E Smith, Samantha J Reznik, Jennifer L Stewart, John J B Allen, Ezra E Smith, Samantha J Reznik, Jennifer L Stewart, John J B Allen

Abstract

Frontal electroencephalographic (EEG) alpha asymmetry is widely researched in studies of emotion, motivation, and psychopathology, yet it is a metric that has been quantified and analyzed using diverse procedures, and diversity in procedures muddles cross-study interpretation. The aim of this article is to provide an updated tutorial for EEG alpha asymmetry recording, processing, analysis, and interpretation, with an eye towards improving consistency of results across studies. First, a brief background in alpha asymmetry findings is provided. Then, some guidelines for recording, processing, and analyzing alpha asymmetry are presented with an emphasis on the creation of asymmetry scores, referencing choices, and artifact removal. Processing steps are explained in detail, and references to MATLAB-based toolboxes that are helpful for creating and investigating alpha asymmetry are noted. Then, conceptual challenges and interpretative issues are reviewed, including a discussion of alpha asymmetry as a mediator/moderator of emotion and psychopathology. Finally, the effects of two automated component-based artifact correction algorithms-MARA and ADJUST-on frontal alpha asymmetry are evaluated.

Keywords: Frontal EEG asymmetry; ICA artifact correction; Signal processing; Statistical models.

Copyright © 2016 Elsevier B.V. All rights reserved.

Figures

Figure A1.
Figure A1.
Histograms of percent of variance rejected for ADJUST (top panel), MARA (middle panel), and the overlap between ADJUST and MARA (bottom panel).
Figure A2.
Figure A2.
Power across all scalp sites (top panel), frontal channels only (F1, F2, F3, F4, F5, F6, F7, F8, and Fz, middle panel), occipital channels only (POz, Oz, O1, and O2, bottom panel) at each spectral point for data that was visually inspected data, visually inspected + ADJUST, and visually inspected + MARA.. All 3 conditions show a robust alpha peak, MARA and ADJUST similarly attenuate delta and theta power, and MARA attenuates high frequency power at frontal channels.
Figure A3.
Figure A3.
Signal-to-noise ratio across all scalp sites (top panel), frontal channels only (F1, F2, F3, F4, F5, F6, F7, F8, and Fz, middle panel), occipital channels only (POz, Oz, O1, and O2, bottom panel) at each spectral point for data that was visually inspected data, visually inspected + ADJUST, and visually inspected + MARA. MARA and ADJUST increase SNR over frontal channels.
Figure A4.
Figure A4.
Spectral power differences for single recordings (N≥2480) across all scalp sites (top panel), frontal channels only (F1, F2, F3, F4, F5, F6, F7, F8, and Fz, middle panel), occipital channels only (POz, Oz, O1, and O2, bottom panel) at each spectral point for data that was visually inspected data, visually inspected + ADJUST, and visually inspected + MARA. MARA and ADJUST produce generally consistent results across recordings. Both ADJUST and MARA adequately preserved alpha power in most, but not all cases. MARA more frequently reduced high frequency power over frontal channels than ADJUST.
Figure A5.
Figure A5.
Histograms of asymmetry scores for different artifact mitigation approaches at a frequently used asymmetry site (F6/F5). Histograms of asymmetry scores following visual inspection (top panel), visual inspection + ADJUST (top-middle panel), and visual inspection + MARA (bottom-middle panel), and the overlap between three artifact approaches (bottom panel) show that MARA reduces the pointedness of the distribution in asymmetry scores around zero compared to visual inspection only, and visual inspection + ADJUST.
Figure A6.
Figure A6.
Correlations for asymmetry scores between different methods of artifact mitigation at four frontal electrodes. Pearson correlations between 3 datasets are presented: visually inspected data, visually inspected + ADJUST, and visually inspected + MARA cleaned data.. Correlations between visually inspected data and visually inspected + component cleaned data were high in every case. Interestingly, the lowest correlations were between visual inspected data and MARA, and between ADJUST and MARA, at those electrodes that also tended to have the greatest high frequency power, presumably from frontalis activity (i.e., F4/F3 and F6/F5).
Figure 1.
Figure 1.
Topography of alpha power under eyes open (top) and eyes closed (bottom) conditions as a function of transformation (Cz, average (AR), or linked mastoid (LM) reference or current source density (CSD) transformation) from a sample of over 2400 recordings. Power values at each site represent natural-log transformed values; thus negative numbers represent mean power values less than one. Each transformation is scaled independently, but within each transformation, eyes open and closed data are plotted on the same scale. Only the CSD transformation contains occipital alpha to occipital leads, whereas the other three montages show reflected alpha at frontal regions, visible most clearly by a comparison of frontal leads under eyes closed compared to eyes open recordings.
Figure 2.
Figure 2.
Overview of converting time-domain signals to power spectra for EEG asymmetry research. Panel A depicts a 10-second segment of raw data from a single channel on the left, and the spectral representation of this epoch on the right. Panel B illustrates the process of epoching the longer segment into shorter overlapping two-second epochs. Panel C shows the impact of the Hamming window (dotted bell curve) on a single epoch, with the grey line representing the raw signal and the black line representing the signal after the application of the window. Note that a discontinuity would result if a copy of the raw (grey) signal were concatenated following this signal, but no such discontinuity would result for a similarly concatenated windowed (black) signal. Panel D displays the net weighting (black line, scaled to fit graph) of overlapping hamming windows (grey lines) for two-second epochs. Panel E illustrates the impact of averaging power spectra. The top 9 grey lines are the spectral representation of 9 two-second epochs, and the lower black line is the average spectrum. Note that alpha power (8–13 Hz) is somewhat variable from epoch to epoch, but that the average spectrum reveals a distinct alpha peak. Vertical axis in Panel E is power in microvolts-squared. Figure after Allen, Coan, and Nazarian (2004).
Figure 3.
Figure 3.
Spectral power across the scalp for five frequency bands of interest before and after automatic IC-based correction using the ADJUST algorithm. The depicted scale (inμV2) varies across frequency bins, and is the same for the first two columns (Visual inspection only, Visual inspection only + ADJUST; the difference score has its own scale)). Overlapping-epochs were hamming-windowed prior to FFT to mitigate edge-artifacts. The FFT results for each epoch were averaged for each subject, then across all subjects (i.e., a grand-average). Spectral points were averaged within canonical frequency bands.
Figure 4.
Figure 4.
Spectral power across the scalp for five frequency bands of interest before and after automatic IC-based correction using the MARA algorithm. The depicted scale (in μV2) varies across frequency bins, and is the same for the first two columns (Visual inspection only, Visual inspection only + MARA; the difference score has its own scale)). Overlapping-epochs were hamming-windowed prior to FFT to mitigate edge-artifacts. The FFT results for each epoch were averaged for each subject, then across all subjects (i.e., a grand-average). Spectral points were averaged within canonical frequency bands.
Figure 5.
Figure 5.
This figure illustrates conceptual differences between the third variables of moderator, mediator, covariate, and confound, adapting diagrams from Baron and Kenny (1986). As described in further detail in the text, a moderator is a third variable that changes the relationship between a predictor and outcome variable. Covariates do not alter but can clarify the relationship between predictor and outcome by adjusting for variance between a third variable and the outcome. On the other hand, mediators are third variables through which a predictor variable changes an outcome variable. Mediators may be distinguished from confounds, which are third variables that share the same statistical relationships but cannot reasonably be the cause by which the predictor affects change.

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