Hybrid Time-Frequency Deep Attention Network for EEG-Based Cognitive State Classification

Abstract

Electroencephalogram (EEG)?based cognitive?state classification remains challenging due to signal non?stationarity and noise. We present a compact hybrid model that integrates residual convolutional blocks for spectral–spatial feature extraction, a bidirectional Long Short-Term Memory (LSTM) for temporal fusion, and multi?head self?attention to weight time–frequency representations. On the PhysioNet Motor Imagery dataset (109 subjects, 64 channels), our approach attains 95.2\% test accuracy, surpassing standalone Convolutional Neural Network (CNN), LSTM and Transformer baselines by 5–15\%.  An ablation study confirms that jointly leveraging convolutional and attention mechanisms is critical for robust performance. Statistical comparison using McNemar's test further supports the reliability of the proposed model, which shows no significant difference compared to a CNN+LSTM+Fusion baseline ($p = 0.19$), and a highly significant improvement over the Transformer-based model ($p < 0.0001$). These results highlight the importance of tailoring model components to the unique properties of EEG data for reliable cognitive-state decoding. These findings highlight the power of attention?driven fusion for reliable EEG decoding.