EEG-CogNet: A deep learning framework for cognitive state assessment using EEG brain connectivity

Abstract

The assessment of cognitive states such as workload, attention, and fatigue is crucial in cognitive science and human performance fields due to its significant impact on work efficiency and decision-making. This paper introduces a deep learning framework for cognitive state assessment using Electroencephalogram (EEG) brain connectivity. The framework was evaluated using EEG data from 26 participants through three computer-based tasks: the Dual N-Back Task, Visual Search Task, and Continuous Performance Task, each designed to induce varying levels of cognitive workload, attention, and fatigue, respectively. A comprehensive dataset was meticulously compiled, including physiological, behavioral, and subjective data to ensure robust analyses. The EEG data underwent rigorous pre-processing and feature extraction processes, focusing on brain connectivity metrics such as Coherence and Phase-Locking Value across multiple frequency bands and employing three neural network architectures: Convolutional Neural Networks (CNN), Graph Convolutional Networks (GCN), and Convolutional Long Short-Term Memory networks (ConvLSTM) for classification. The proposed work achieved classification accuracies of 96.53% for workload, 98.40% for attention, and 97.86% for fatigue in the combined frequency bands. The study’s findings underscore the potential of EEG-based methods for non-invasive cognitive state monitoring. By addressing existing limitations such as small sample sizes and task-specific models, this research enhances the generalizability and applicability of EEG-based cognitive assessments. The implications of these advancements are significant for fields requiring continuous cognitive monitoring, such as defense, healthcare, and high-risk operational environments.