Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction

• Published in: Brain sciences Vol. 14; no. 3; p. 271
• Main Authors: Zhang, Jinhao, Hao, Yanrong, Wen, Xin, Zhang, Chenchen, Deng, Haojie, Zhao, Juanjuan, Cao, Rui
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2024; MDPI
• Subjects: adjacency matrix; Artificial intelligence; Brain; Cognitive ability; Computational linguistics; Credit card processing services; deep learning; EEG; electroencephalogram; Electroencephalography; Emotions; frequency band; Language processing; Natural language interfaces; Neural networks; Research methodology; SEED dataset; Social networks; subject-independent; SEED dataset; deep learning; subject-independent; adjacency matrix; electroencephalogram; frequency band
• ISSN: 2076-3425; 2076-3425
• DOI: 10.3390/brainsci14030271

Emotion is one of the most important higher cognitive functions of the human brain and plays an important role in transaction processing and decisions. In traditional emotion recognition studies, the frequency band features in EEG signals have been shown to have a high correlation with emotion production. However, traditional emotion recognition methods cannot satisfactorily solve the problem of individual differences in subjects and data heterogeneity in EEG, and subject-independent emotion recognition based on EEG signals has attracted extensive attention from researchers. In this paper, we propose a subject-independent emotion recognition model based on adaptive extraction of layer structure based on frequency bands (BFE-Net), which is adaptive in extracting EEG map features through the multi-graphic layer construction module to obtain a frequency band-based multi-graphic layer emotion representation. To evaluate the performance of the model in subject-independent emotion recognition studies, extensive experiments are conducted on two public datasets including SEED and SEED-IV. The experimental results show that in most experimental settings, our model has a more advanced performance than the existing studies of the same type. In addition, the visualization of brain connectivity patterns reveals that some of the findings are consistent with previous neuroscientific validations, further validating the model in subject-independent emotion recognition studies.