# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
## 3 Decoding Results
The \(x\)-axis represents increasing network depth, shown as the ratio of convolutional layers to total layers. This visualization allows for the observation of how the model's performance on unseen data evolves with increasing complexity, providing crucial insights into the optimal architecture for the EEG classification task. As evident from the figure, accuracy shows a consistent upward trend as the network depth increases, stabilizing at around 90% for the more complex architectures. Precision and recall, however, exhibit more nuanced behaviors. Precision peaks with the 9/39 architecture before slightly declining, while recall shows the most variability, reaching its highest point with the 11/47 configuration. Interestingly, the 7/31 architecture emerges as a compelling balance between performance and complexity. While not achieving the highest individual scores, it demonstrates robust performance across all metrics without the computational overhead of deeper networks. The 7/31 model maintains high accuracy (>90%) while offering a favorable trade-off between precision and recall. Furthermore, the marginal  
Figure 8: Training and validation recall across iterations for the CNN model in EEG signal classification. Figure 9: CNN test set performance metrics for EEG classification across varying network depths. gains in performance beyond the 7/31 architecture do not justify the significant increase in computational resources and training time required for deeper networks. This analysis not only provides valuable insights into the trade-offs between model complexity and performance but also guides our decision to select the 7/31 architecture as the optimal configuration for EEG signal classification in the 30-40 Hz range. Leveraging this optimized architecture, our CNN-based approach for detecting specific brain activity patterns in the 30-40 Hz range of EEG data demonstrated promising results across training, validation, and test sets. The training set performance showed high accuracy at 91.93%, with precision and recall closely balanced at 73.56% and 73.14%, respectively. This high accuracy indicates that the model learned to distinguish effectively between the two classes (high and low gamma activity) during the training phase. The balanced precision and recall suggest that the model performed consistently in identifying positive cases and avoiding false positives during training. This balance is crucial, as it indicates that the model is not biased towards over-predicting or under-predicting the presence of high gamma activity. The relatively high values across all three metrics suggest that the model has captured meaningful patterns in the EEG data related to cognitive states. Moving to the validation set, we observed a slight decrease in accuracy to 89.98%. While the recall remained similar to the training set at 73.66%, there was a more substantial drop in precision to 64.99%. This pattern suggests that the model may be overfitting slightly to the training data, leading to more false positives when presented with new data in the validation set. The maintenance of recall indicates that the model is still effectively identifying true positive cases, but the decrease in precision suggests it is also incorrectly classifying some negative cases as positive. This slight performance drop is not unusual when moving from training to validation data and highlights the importance of using techniques such as regularization and early stopping to mitigate overfitting. The test set results demonstrate the model's ability to generalize to completely unseen data, which is crucial for real-world applications. The accuracy on the test set was 91.42%, comparable to that of the training set, indicating robust performance. Precision and recall values were well-balanced at 71.41% and 72.51%, respectively, showing improvement over the validation set. This improvement suggests that the model has good generalization capabilities and has not overfitted to the validation data.