The categorization of cognitive and resting states derived from electroencephalography (EEG) signals is crucial for comprehending fluctuations in brain activity linked to various mental states.
EEG provides a non-intrusive approach for documenting brain function in both resting and task-oriented cognitive conditions, whilst deep learning techniques enable the automatic extraction of significant patterns from intricate EEG data.
This study presents a deep learning framework to distinguish between resting and cognitive states through EEG records.
The proposed framework integrates a Convolutional Neural Network (CNN) stacked with a Gated Recurrent Unit (GRU) for the extraction of features from EEG signals.
Time-frequency analysis is conducted to explore the salient aspects of signals, and the derived features are then assessed utilizing conventional deep learning and machine learning classifiers, including the suggested 2D-Net architecture.
The proposed approach and feature extraction strategy outperform the evaluated comparative methods, achieving accuracies of 83.177% for resting-versus-mathematical task classification, 76.107% for resting-versus-memory task classification, and 83.432% for resting-versus-music task classification.
The findings illustrate the efficacy of integrating signal processing with deep learning methodologies to discriminate resting from cognitive states utilizing EEG signals.