# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
## 2 Methodology
### Data Pre-Processing  
The raw EEG data underwent a series of preprocessing steps to enhance signal quality and isolate the relevant neural activity. These steps were implemented using MATLAB and the EEGLAB toolbox (version 2024.0) [5], a widely used software package for EEG data analysis. The preprocessing pipeline began with data import and initial processing, which included loading cnt files using EEGLAB's import function for ANT EEProbe.cnt files. Channel locations were then imported using a standardized electrode location file, which provides precise spatial information for each electrode. Following this, the data was re-referenced to the average of all scalp electrodes. This re-referencing step, while maintaining the original channel count, effectively reduced the data rank by one. Independent component analysis (ICA) decomposition was then performed using the extended infomax algorithm with the 'extended' parameter set to 1. The ICA decomposition was designed to automatically detect and adjust to the reduced rank of the data resulting from the average referencing step, ensuring an appropriate analysis of the re-referenced data [29; 30]. Artifact rejection and cleaning were carried out using the clean raw data plugin, which incorporates artifact subspace reconstruction (ASR). This process applied specific criteria for flatline detection (10 s), channel correlation (0.7), line noise (4 standard deviations), burst detection (20 standard deviations), and window rejection (0.25 standard deviations). Bad data periods were also removed during this stage. The final step involved frequency-specific filtering using a FIR bandpass filter. This filter was applied with a low edge frequency of 26.5 Hz and a high edge frequency of 45 Hz, using a filter order of 200. The DC offset was removed as part of this filtering process. This comprehensive preprocessing pipeline was designed to isolate the frequency range of 26.5-45 Hz, which encompasses the gamma band of interest in this study, while effectively removing artifacts and improving signal quality. This comprehensive approach ensured the data were optimally prepared for subsequent analysis of gamma band activity (30-40 Hz) in the inferior parietal lobule region. Figure 1 illustrates the topographical distribution of EEG channels, with particular emphasis on specific channels highlighted with white backgrounds (3 LD, 4 LD, 4 LC, 5 LC). These channels, located in the inferior parietal lobule region, were of specific interest in our study due to their association with cognitive processes relevant to our research objectives. The figure employs a continuous color spectrum ranging from red through orange, yellow, green, to light blue, typically corresponding to varying levels of neural activity or power in EEG representations. Areas in red and orange generally indicate higher activity, blue regions suggest lower activity, and green and yellow represent intermediate levels. The central and parietal regions, including the highlighted channels, display cooler colors (light blue to green), potentially indicating relatively lower activity in these areas of interest, while the periphery shows warmer colors (yellow to red), suggesting higher activity in the frontal and occipital regions. To fully leverage the complex spatial and temporal information captured by the EEG electrodes, event-related spectral perturbation (ERSP) analysis was conducted on epochs ranging from \(-100\) ms to \(900\) ms relative to the event, focusing on the 30-40 Hz gamma frequency band. Wavelet transformation was applied with cycles set to [3 0.5] at the lowest and highest frequencies, yielding 191 output times. Baseline correction used the \(-200\) ms to \(0\) ms period, with absolute power values for scaling. The analysis, performed on four channels in the inferior parietal lobule region, employed a binary classification scheme based on the 85 th percentile of mean power across the 30-40 Hz range. Statistical  
Figure 1: Topographical distribution of EEG electrode placement.