An EEG Emotion Recognition and Classification Method Based on MSRA and Bidirectional LSTM

EEG-based emotion recognition faces challenges from signal non-stationarity, low signal-to-noise ratios, inter-subject variability, and ambiguous emotional boundaries. To address these issues, this study proposes an approach that combines the Multi-Taper Method (MTM) and Welch method for power spectral density (PSD) feature extraction and combines the Multi-Scale Residual Attention and Bidirectional Long Short-Term Memory (MSRA-BiLSTM) for emotion recognition. The approach captures spatial and frequency-band features while learning temporal dependencies. Experiments on three-class classification using the DEAP dataset achieved accuracies of 98.03%, 97.54%, and 96.81% for Valence, Arousal, and Dominance, respectively, with Macro-F1 scores of 0.9789, 0.9749, and 0.9674. The overall accuracy was 97.46%, outperforming conventional Welch-based features, differential entropy features, and end-to-end training. Therefore, the study has contributed to the problem of emotion recognition based on EEG signals.