EEG-recording and processing
We recorded EEG from 128 scalp electrodes, using the Active Two Biosemi system, arranged according to the 10-5 system (Oostenveld & Praamstra, 2001). We recorded electrooculogram vertically, with one electrode just above the eyebrow and one approximately 3 cm below the eye. EEG was recorded with a sampling rate of 512 Hz.
We recorded eyes-closed resting state EEG for two minutes both prior to starting, and after completion of the computerized perceptual task. Participants were asked to relax and sit still during this period. The EEG recordings were then cut into 1-second, non-overlapping epochs. Pre-stimulus-EEG data was epoched from -700 – 0 ms relative to stimulus onset. Epoched data was then detrended and we spherically interpolated an average of 2.9% of electrodes for eyes-closed data and an average of 3.4% of electrodes for pre-stimulus data.
We used Matlab (Mathworks, 2022) for the pre-processing of all EEG-recordings, using custom Matlab scripts. Current source density (CSD) transformation is used in many EEG studies in order to reduce volume conduction effects and signal mixing (Kayser & Tenke, 2006; 2015). This step was not carried out in Samaha & Postle’s original study. We therefore opted to analyse the data both with- and without applying CSD transformation in order to determine its impact.
For the CSD-transformed eyes-closed EEG data, we rejected epochs when voltages exceeded a CSD-transformed amplitude threshold of 500 µV m2 for both scalp and eye electrodes. An average of 47 (standard deviation (SD) 53.5) epochs were rejected per participant. For CSD-transformed pre-stimulus EEG data, we rejected epochs when voltages exceeded a threshold of 500 µV m2 for scalp electrodes or 100 µV for eye electrodes (eye electrodes were not included in the CSD-transformation and therefore required a lower threshold). We rejected an average of 125 (SD 108.2) epochs per participant.
For the non-CSD-transformed eyes-closed EEG-data, we rejected epochs when voltages exceeded a threshold of 100 µV for scalp electrodes and 500 µV for eye electrodes. We rejected an average of 32 (SD 28) eyes-closed EEG-data epochs per participant. For non-CSD-transformed pre-stimulus EEG-data, we rejected epochs when voltages exceeded 100 µV for all electrodes. We rejected an average of 52 (SD 58.8) epochs of pre-stimulus EEG-data per participant.
Epochs were then multiplied by a hamming window and fast Fourier transformed with a frequency resolution of 0.1 Hz. Individual alpha peak frequency was defined as in Samaha & Postle and was extracted from the highest amplitude in the power spectrum within the frequency range 8 – 13 Hz. All participants showed a clear peak within this range at all scalp electrodes for eyes-closed data. Similar to the original study, not all electrodes showed a peak in the 8 – 13 Hz frequency range and no electrode showed a peak for all participants for pre-stimulus data. We therefore followed the original methodology and chose whichever electrode had the highest power amplitude within the alpha band range for each participant.
Because it has been shown that peak alpha frequency can change over time within an individual (Cohen, 2014), we computed instantaneous alpha to assess if pre-stimulus alpha frequency is higher prior to correct task responses compared to incorrect responses. Instantaneous alpha corresponds to the first temporal derivative of the phase angle time series. To calculate instantaneous alpha, we replicated Samaha & Postle’s method, and chose the electrode that had the highest amplitude in the alpha band range for the pre-stimulus recordings at the group level, which we interpreted as the electrode that had the highest alpha amplitude for most participants: electrode C16. Following our same reasoning as with the previous analysis, we again also computed instantaneous alpha frequency for electrode B7. We epoched the pre-stimulus EEG-recordings from -700 to 0 ms relative to stimulus onset, and then copied, flipped and appended each epoch to avoid edge artifacts. Using code developed by Cohen (2014), we then defined frequency boundaries of 8 – 13 Hertz, and applied a zero-phase bandpass filter with 15% transition zones. We extracted the analytic signal with a Hilbert transform and median filtered using default parameters set by Cohen: we applied the median filter ten times with different window sizes ranging from 10 to 400 ms, in steps of 10 ms. As Samaha & Postle only reported instantaneous frequency for the 500 ms prior to stimulus onset, we followed their method and only calculated instantaneous alpha for the timepoints ranging from -500 – 0 ms.