文献类型：期刊文献

英文题名：Neuron Perception Inspired EEG Emotion Recognition With Parallel Contrastive Learning

作者：Li, Dongdong[1];Huang, Shengyao[1];Xie, Li[1];Wang, Zhe[1];Xu, Jiazhen[2]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Qingdao Univ, Affiliated Hosp, Canc Inst, Qingdao 266000, Shandong, Peoples R China

年份：2025

卷号：36

期号：8

起止页码：14049

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20251218066940);WOS:【SCI-EXPANDED(收录号:WOS:001470643800001)】；

基金：This work was supported by the Natural Science Foundation of China under Grant 62276098 and Grant 62376095.

语种：英文

外文关键词：Electroencephalography; Visualization; Brain modeling; Streams; Feature extraction; Emotion recognition; Contrastive learning; Adaptation models; Visual perception; Physiology; electroencephalogram (EEG); emotion recognition; multisource domain adaptation (DA); visual perception

摘要：Considerable interindividual variability exists in electroencephalogram (EEG) signals, resulting in challenges for subject-independent emotion recognition tasks. Current research in cross-subject EEG emotion recognition has been insufficient in uncovering the shared neural underpinnings of affective processing in the human brain. To address this issue, we propose the parallel contrastive multisource domain adaptation (PCMDA) model, inspired by the neural representation mechanism in the ventral visual cortex. Our model employs a neuron-perception-inspired contrastive learning architecture for EEG-based emotion recognition in subject-independent scenarios. A two-stage alignment methodology is employed for the purpose of aligning numerous source domains with the target domain. This approach integrates a parallel contrastive loss (PCL) which simulates the self-supervised learning mechanism inherent in the neural representation of the human brain. Furthermore, a self-attention mechanism is integrated to extract emotion weights for each frequency band. Extensive experiments were conducted on three publicly available EEG emotion datasets, SJTU emotion EEG dataset (SEED), database for emotion analysis using physiological signals (DEAP), and finer-grained affective computing EEG dataset (FACED), to evaluate our proposed method. The results demonstrate that the PCMDA effectively utilizes the unique EEG features and frequency band information of each subject, leading to improved generalization across different subjects in comparison to other methods.