# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
## 5 Conclusions  
Our study demonstrates an advancement in EEG signal analysis through the application of CNNs to the 30-40 Hz gamma band. The development of a CNN-based model for cognitive state classification, achieving 91.42% accuracy on the test set, shows improvement over some traditional machine learning approaches in this domain. The preprocessing pipeline integrating ERSP analysis with binary classification based on power thresholds effectively prepares complex EEG signals for machine learning analysis. The CNN architecture, tailored for EEG data, captures both fine-grained spectral features and broader patterns in brain signals. The consistent performance across training, validation, and test sets indicates learning and generalization capabilities. The balance between precision and recall suggests that our model is capable of identifying true positives while limiting false positives, which is valuable in EEG analysis where accurate detection and minimal false alarms are important. These results indicate potential for application in research and clinical settings that require detection of specific brain activity patterns. In the realm of behavioral sciences and neuroscience, our approach offers a more efficient method for analyzing brain activity patterns associated with specific behaviors and cognitive processes. This could lead to a more nuanced understanding of the neural correlates of various cognitive and behavioral processes, learning processes, and the effects of various interventions on neural activity. The ability to quickly process and classify EEG data could be particularly valuable in studies of neurological disorders, where identifying subtle changes in brain activity patterns is crucial. Furthermore, this approach has potential applications in the field of neuroeducation. By providing a more efficient way to analyze brain activity during learning tasks, it could help in developing more effective teaching methods tailored to individual cognitive patterns. This could lead to the design of adaptive learning environments that respond in real-time to a student's cognitive state, optimizing the learning process. Additionally, in the context of neurofeedback and cognitive training, our method could offer more precise and timely feedback, potentially enhancing the effectiveness of these interventions.  
[PERSON] conceived the project and provided overall supervision. [PERSON] led the experimental work, organized the EEG data, and developed the core idea with the support and guidance of [PERSON], E.N. and G.C.L. conducted the simulations with assistance from [PERSON], [PERSON], E.N., G.C.L. and D.M. drafted the manuscript and response letter. E.N., G.C.L. and N.A. prepared the figures. All authors contributed to the review and assessment of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.