Gated transformer network based EEG emotion recognition

• Published in: Signal, image and video processing Vol. 18; no. 10; pp. 6903 - 6910
• Main Authors: Bilgin, Metin, Mert, Ahmet
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2024; Springer Nature B.V
• Subjects: Band spectra; Computer Imaging; Computer Science; Datasets; Electroencephalography; Emotion recognition; Emotional factors; Emotions; Frequencies; Image Processing and Computer Vision; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Vision; Emotion recognition; Time-series; Transformer; Gated transformer network
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-024-03360-5

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 ( δ , θ , α , β , γ ) 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 × 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.