Neuron Perception Inspired EEG Emotion Recognition With Parallel Contrastive Learning

• Published in: IEEE transaction on neural networks and learning systems Vol. 36; no. 8; pp. 14049 - 14062
• Main Authors: Li, Dongdong, Huang, Shengyao, Xie, Li, Wang, Zhe, Xu, Jiazhen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2025
• Subjects: Adaptation models; Algorithms; Brain modeling; Contrastive learning; Databases, Factual; electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Emotion recognition; Emotions - physiology; Feature extraction; Humans; Machine Learning; multisource domain adaptation (DA); Neural Networks, Computer; Neurons - physiology; Physiology; Streams; Visual perception; Visualization
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2025.3546283

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.