# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
## 1 Introduction  
Electroencephalography (EEG) has emerged as a cornerstone technology in neuroscience, offering real-time insights into brain activity through non-invasive recording of neuronal electrical signals [1]. This technique has revolutionized our understanding of brain function and has found applications in various fields, from clinical diagnostics to brain-computer interfaces [2]. The present study aims to harness EEG data to classify brain states during cognitive tasks, focusing on the frequency range of 30-40 Hz, which includes part of the gamma band associated with high-level cognitive functions [3, 4]. Despite these advancements, current EEG analysis procedures, particularly when using popular software such as EEGLAB (version 2024.0) [5], remain largely manual and time-consuming. Researchers often spend significant time on preprocessing, feature identification, and interpretation of complex spectral patterns, which can slow down research progress and limit the volume of data analyzed. Our study is motivated by the need to accelerate and automate these analytical procedures, allowing researchers to focus more on interpreting results and generating insights. By developing an automated approach for cognitive state classification, we aim to provide a tool that can significantly reduce the time and effort required for EEG analysis, potentially accelerating discoveries in cognitive neuroscience and improving clinical applications of EEG technology. Building on this foundation, recent advancements in EEG technology have enabled more precise and reliable data collection, allowing researchers to explore subtle changes in brain activity across different cognitive states [6]. The gamma band, typically defined as oscillations between 30 and 100 Hz, hasbeen of particular interest due to its association with various cognitive processes, including attention, memory, and perceptual binding [7, 8]. Our focus on the 30-40 Hz range allows us to capture a significant portion of this band while minimizing potential muscle artifacts that can contaminate higher frequencies [9]. To further refine our approach, four specific EEG channels corresponding to electrode positions over the inferior parietal lobule were chosen for analysis. This region is critically involved in higher-order cognitive processes such as working memory, attention, and executive function [10, 11]. By focusing on these channels, the study aimed to capture the most relevant neural activity associated with cognitive tasks. The inferior parietal lobule has been implicated in numerous studies as a key area for cognitive control and decision-making [12, 13], making it an ideal target for our investigation. To analyze this complex data, our approach leverages CNNs, which have shown remarkable success in various domains, including image classification and natural language processing [14, 15]. In the field of EEG analysis, CNNs have demonstrated superior performance compared to traditional machine learning methods in tasks such as motor imagery classification, seizure detection, and mental workload estimation [16]. The choice of a CNN model for our study was also driven by its compatibility with emerging optical computing architectures that integrate CNN networks into optical chips. This alignment offers advantages for EEG analysis, including improved processing speed and energy efficiency. The optical CNN architecture allows for parallel processing of multiple inputs, ideal for EEG data's multiple channels and time-frequency representations. The spatial nature of EEG data aligns well with CNN's 2D convolution operations, enabling effective capture of local patterns. The scalability and noise-resilience of optical CNN implementations are beneficial for EEG analysis, allowing for more complex models and robust performance. This hardware-based approach facilitates real-time processing of massive datasets, offering low-volume 3D connectivity with large bandwidth and minimal heat production compared to electronic implementations [17].