Title: Psychological health detection based on transformer stress EEG signal recognition model

Authors: Hongying Zhang

Addresses: Counseling and Psychological Services, Shanghai Jiao Tong University, Shanghai, China

Abstract: In response to the problem of emotion recognition implied in electroencephalogram signals, this study conducts psychological health testing based on electroencephalogram signal recognition. Firstly, position embedding is added to the electroencephalogram signals, and a transformer-based electroencephalogram signal recognition model is constructed. Then, using the attention mechanism to improve the long short-term memory network, an emotion recognition model based on the bidirectional network is constructed. The results demonstrated that the proposed model had high recognition accuracy on both datasets, with 94.67% and 95.06% respectively, and recall rates of over 90%. The recognition accuracy (91.46%) and F1 score (90.77%) of the complete model were the highest. The proposed emotion recognition model had the highest recognition accuracy (98.14%), the smallest loss value (0.02), and the fastest decline rate. The research results contribute to the development of psychological health monitoring technology.

Keywords: transformer; EEG signals; psychological health; multi-head attention; MHA; long short-term memory; LSTM.

DOI: 10.1504/IJBIC.2026.155401

International Journal of Bio-Inspired Computation, 2026 Vol.28 No.1, pp.45 - 52

Received: 22 Oct 2024
Accepted: 18 Sep 2025
Published online: 31 Jul 2026 *

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