EEG Emotion Recognition Combining SCHO Channel Selection and Top-k Attention
[1]MENG Yinghui,SONG Jiaoshuai,LI Duan,et al.EEG Emotion Recognition Combining SCHO Channel Selection and Top-k Attention[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-9.[doi:10. 13705 / j. issn. 1671-6833. 2027. 02. 002]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
48
Number of periods:
2027 XX
Page number:
1-9
Column:
Public date:
2027-12-10
- Title:
- EEG Emotion Recognition Combining SCHO Channel Selection and Top-k Attention
- CLC:
- TN911. 7 R318
- Abstract:
- EEG-based emotion recognition was an important research topic in brain-computer interfaces, affective computing, and mental health monitoring. However, existing EEG emotion recognition systems generally suffered from a large number of electrodes, high acquisition costs, limited wearability, and performance degradation under low-channel conditions. To address these issues, a low-channel EEG emotion recognition algorithm was proposed, in which the meta-heuristic optimization algorithm Sinh Cosh Optimizer ( SCHO) was integrated with the convolutional residual Top-k attention framework ( CRTA) to achieve adaptive channel selection and effective feature modeling. First, fast Fourier transform and power spectral density features were extracted from EEG signals, and SCHO was employed to adaptively select subject-specific emotion-discriminative electrode subsets. Subsequently, multiscale features were captured through convolutional layers and residual blocks in CRTA, while an improved Top-kmulti-head sparse attention mechanism was introduced in deep layers to reduce redundant information and enhance emotion-related features. Experimental results on the SEED and DEAP datasets demonstrated that the proposed algorithm achieved competitive performance under both full-channel and low-channel settings. Furthermore, a fixed six-electrode configuration consisting of F3, F8, T7, T8, P3, and P4 was found to maintain high recognition accuracy, providing an effective solution for low-channel and high-performance EEG emotion recognition and offering a reference for lightweight wearable EEG emotionmonitoring systems.
- References:
- -
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Last Update:
2026-10-30
