Online Multi-level Contrastive Representation Distillation for Cross-Subject fNIRS Emotion Recognition
<|reference_start|>Online Multi-level Contrastive Representation Distillation for Cross-Subject fNIRS Emotion Recognition: Utilizing functional near-infrared spectroscopy (fNIRS) signals for emotion recognition is a significant advancement in understanding human emotions. However, due to the lack of artificial intelligence data and algorithms in this field, current research faces the following challenges: 1) The portable wearable devices have higher requirements for lightweight models; 2) The objective differences of physiology and psychology among different subjects aggravate the difficulty of emotion recognition. To address these challenges, we propose a novel cross-subject fNIRS emotion recognition method, called the Online Multi-level Contrastive Representation Distillation framework (OMCRD). Specifically, OMCRD is a framework designed for mutual learning among multiple lightweight student networks. It utilizes multi-level fNIRS feature extractor for each sub-network and conducts multi-view sentimental mining using physiological signals. The proposed Inter-Subject Interaction Contrastive Representation (IS-ICR) facilitates knowledge transfer for interactions between student models, enhancing cross-subject emotion recognition performance. The optimal student network can be selected and deployed on a wearable device. Some experimental results demonstrate that OMCRD achieves state-of-the-art results in emotional perception and affective imagery tasks.<|reference_end|>
@article{lai2024online,
    title={Online Multi-level Contrastive Representation Distillation for
  Cross-Subject fNIRS Emotion Recognition},
    author={Zhili Lai, Chunmei Qing, Junpeng Tan, Wanxiang Luo, Xiangmin Xu},
    journal={arXiv preprint arXiv:2409.16081},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16081},
    primaryClass={cs.HC cs.AI}
}