Eeglab toolbox

While participants performed the Stroop task, EEG data were recorded from 32 Ag/AgCl scalp electrodes (BrainCap, GmbH, Germany) according to the international 10–20 system. The placement of the recording reference was at Cz, while the ground was positioned at approximately AFz. The impedances were kept below 10 kΩ with the sampling frequency at 1,000 Hz.
Utilizing MATLAB (MathWorks, Natick, MA, United States) and the EEGLAB toolbox (Delorme and Makeig, 2004 ), we processed the continuous EEG data with in-house scripts. An offline digital band-pass filter (0.1–30 Hz) was applied. Epochs were extracted from −200 to 1,000 ms relative to the onset of the word stimulus and baseline corrected using the prestimulus interval (−200 to 0 ms). Independent component analysis (ICA) was used to correct eye movement, muscle artifacts, and heartbeat artifacts. All EEG epochs were processed for artifact detection by visual inspection and EEGLAB, and detection of obvious eye blinks and epochs with amplitude values exceeding ±100 mV at any electrode were rejected and later re-referenced to the average reference (Tafuro et al., 2019 (link); Overbye et al., 2021 (link)). To guarantee the quality of data, patients with >20% of bad epochs for each condition and/or five bad channels were removed from the analysis, and one MAP- participant with more than 20% of bad epochs was excluded.

The significant difference between the degree centralities at the nodes for KMI and VMI was calculated for each frequency band using a non-parametric permutation test. The significance was evaluated by the permutation distribution with 10,000 iterations^{40 (link)}. Pearson's correlation coefficient (R) was calculated to measure the similarity between the connectivity matrixes of the different conditions. All analyses were conducted with MATLAB (R2018a, Math-Works, Natick, MA, USA) and the EEGLAB toolbox^{41 (link)}.

An actiCHamp amplifier (Brain Vision LLC, NC, United States) was used to amplify and digitize the EEG data at a sampling frequency of 512 Hz. The EEG data were stored in a PC running Windows 7 (Microsoft Corporation, Washington, DC, United States). EEG activity was recorded from 64 positions with active Ag/AgCl scalp electrodes (actiCAP electrodes, Brain Vision LLC, NC, United States). The ground and reference electrodes were placed on AFz and on FCz, respectively (see Figure 1).
Electroencephalography acquisition was carried out by NeuroRT Studio software (Mensia Technologies SA, Paris, France). The EEG signal processing procedure was performed using MATLAB functions (MathWorks Inc., Natick MA, United States), specifically the EEGLab toolbox (Delorme and Makeig, 2004 (link)). EEG microstates were extracted and characterized by LORETA-KEY v20170220 software (the Key Institute for Brain-Mind Research, Zurich, Switzerland). Statistical analyses were performed by SPSS for Windows, version 23.0 (IBM Inc., Chicago, IL, United States).

EEG data were acquired by an amplifier (actiCHamp, Brain Products GmBH, Gilching, Germany) with 32-channel wet electrodes, with a sampling rate of 500 Hz. Ground and reference electrodes were attached at the left and right mastoid, respectively. EEG data were preprocessed through a pipeline set in the following order: high-pass filtering (>0.5 Hz), bad channel rejection, common average re-referencing, low-pass filtering (<50 Hz), and artifact subspace reconstruction (ASR). EEG data were epoched from −200 to 600 ms after stimulus onset, and baseline correction was conducted with baseline data from −200 ms to onset. After epoching, EEG data were standardized by removing the mean and scaling to unit variance. For preprocessing, EEGLAB Toolbox [20 (link)], MATLAB (The MathWorks, Inc., Natick, MA, USA), and Python (Python Software Foundation, Beaverton, OR, USA) were used together.

EEG data were analysed via custom routines based on EEGLAB toolbox [36 (link)] and Matlab R2017b (MathWorks, Natick, MA, USA). Continuous EEG signals were band-pass filtered between 7 and 25 Hz (with the exclusion of the sigma frequency band by notch filter centered on 13 Hz with a bandwidth at 4 Hz) and between 0.3 and 4.5 Hz, in order to identify the fast and the slow components of the CAP A phases, respectively [29 (link)].

Electroencephalographic data were recorded from a 64-electrode scalp cap using the 10–20 system (Brain Products, Munich, Germany) with reference electrodes on the left and right mastoids. The vertical electrooculogram (EOG) was recorded with placed above and below the left eye. EEG and EOG activity was amplified at 0.01–100 Hz band-pass and sampled at 500 Hz. All of the electrode impedances were maintained below 5 kΩ.
The EEG data were pre-processed and analyzed using Matlab R2011b software (MathWorks) and the EEGLAB toolbox (Delorme and Makeig, 2004 (link)). The EEG data for each electrode were down-sampled to 250 Hz and re-referenced to the grand averages. The signal was then passed through a 0.01- to 30-Hz band-pass filter. Time windows of 200 ms before and 700 ms after the onset of the picture were segmented. EOG artifacts were corrected using an independent component analysis (ICA) (Jung et al., 2001 (link)) (Supplementary Figure S1). Epochs with amplitudes that exceeded ±50 μV at any electrode were excluded from the average (5.6 ± 0.6% trials were excluded).

Each participant could perform routine long-term EEG recording. We used the Nicolet EEG machine to record the EEG at a sampling rate of 500 Hz. The scalp electrodes were placed under the international 10–20 montage system, and the A1 and A2 electrodes were used as references. EEG was performed in the same recording room using the same system, and the same EEG technician used conventional measurement techniques to determine the electrodeposition. We collected EEG for at least 15 h with the subjects relaxed, asleep, and their eyes closed to avoid disturbance. All EEG records contained 19 scalp electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2) and a visual inspection by EEG technician was performed.
We extracted the ictal phase data from raw EEG of PE group under the guidance of professional neurology clinicians; as a result, we had only various ictal phases linked together of each PE participant in the EEG data. To reduce data volume and speed up computation, we down-sampled the data to 100 Hz. Then, the data was filtered with band-pass at frequencies of 0.5 and 45 Hz. Finally, automated artifact removal was performed on the manually processed dataset using the independent components algorithm (ICA). These preprocessing steps were operated using EEGLab toolbox [18 (link)] in MATLAB (MathWorks).