# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
## 3 Decoding Results  
The results of our study demonstrate the effectiveness of using EEG data in the 30-40 Hz gamma band range for classifying cognitive states associated with high-level cognitive functions. Figure 3 demonstrates topographical EEG activity patterns across three subjects, highlighting individual variations in brain activation during cognitive tasks. These visualizations emphasize the spatial distribution of neural activity, with warmer colors indicating higher activation levels. The observed inter-subject variability in activation patterns underscores the importance of considering individual differences in EEG analysis and supports the need for advanced classification techniques such as the CNN model developed in this study. To further analyze the EEG data, ERSP analysis was conducted on the preprocessed data to examine spectral power changes over time and frequency. This analysis employs time-frequency decomposition methods to provide a high-resolution time-frequency representation of the EEG signal. The ERSP analysis examined spectral power changes in the 30-40 Hz range from \(-100\) ms to \(900\) ms relative to the event, using a \(-200\) ms to \(0\) ms baseline, with results expressed as percentage changes from baseline power to quantify event-related oscillatory activity in the gamma band. As illustrated in Figure 4, the ERSP plot reveals patterns of spectral power changes across time and frequency within the 30-40 Hz range. The ERSP plot reveals significant gamma band power increases around 500 ms and 800 ms post-stimulus (represented by warm colors), suggesting event-related synchronizations that may indicate heightened neural processing or cognitive engagement, with the later activity potentially reflecting sustained or secondary processing stages. Figure 3: Topographical EEG activity patterns across three subjects showing individual variations in brain activation. To fully leverage the insights provided by the ERSP analysis, it is crucial to correlate these time-frequency representations with the original EEG signal timestamps. This correlation is achieved by aligning each column in the ERSP plot with specific time points relative to experimental events marked in the original EEG data. By identifying when a feature occurs in the ERSP relative to an event and locating the corresponding event marker in the original EEG data, researchers can pinpoint where spectral changes occur in the continuous EEG recording. This alignment process creates a bridge between the time-frequency analysis and the raw EEG data, enabling an understanding of how spectral dynamics correspond to ongoing EEG activity throughout the experiment. Building upon this time-frequency representation, we developed a method to translate the continuous ERSP data into a binary classification of brain states. For each time point in the ERSP output, we calculated the mean power across the 30-40 Hz frequency range. This frequency-averaged power was compared to a threshold set at the 85 th percentile of all power values in the dataset, a value chosen empirically after extensive testing to optimally balance sensitivity and specificity in detecting significant gamma activity. Time points where the mean power exceeded this threshold were classified as '1', indicating high gamma activity, while those below were classified as '0', representing baseline or lower gamma activity states. Figure 5 presents the result of this binary classification, where black regions represent time-frequency points exceeding the threshold, corresponding to periods of high gamma activity. Our automated approach significantly accelerates the analysis process compared to traditional manual methods in EEGLAB. To demonstrate the superiority of our method compared to the traditional EEGLAB approach, we conducted a comparative analysis of processing times for various numbers of subjects (n).