Gated transformer network based EEG emotion recognition

Multi-channel Electroencephalogram (EEG) based emotion recognition is focused on several analysis of frequency bands of the acquired signals. In this paper, spectral properties appeared on five EEG bands ( \(\delta \) $\delta$ , \(\theta \) $\theta$ , \(\alpha \) $\alpha$ , \(\beta \) $\beta$ , \(\gamma \) $\gamma$ ) and gated transformer network (GTN) based emotion recognition using EEG signal are proposed. Spectral energies and differential entropies of 62-channel signals are converted to 3D (sequence-channel-trial) form to feed the GTN. The GTN with enhanced gated two tower based transformer architecture is fed by 3D sequences extracted from SEED and SEED-IV emotional datasets. 15 participants’ states in session 1–3 are evaluated using the proposed GTN based sequence classification, and the results are repeated by \(3\small \times \) $3\times$ shuffling. Totally, 135 times training and testing are performed on each dataset, and the results are presented. The proposed GTN model achieves mean accuracy rates of 98.82% on the SEED dataset and 96.77% on the SEED-IV dataset for three and four emotional state recognition tasks, respectively. The proposed emotion recognition model can be employed as a promising approach for EEG emotion recognition.