A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces
<|reference_start|>A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces: ERP-based EEG detection is gaining increasing attention in the field of brain-computer interfaces. However, due to the complexity of ERP signal components, their low signal-to-noise ratio, and significant inter-subject variability, cross-subject ERP signal detection has been challenging. The continuous advancement in deep learning has greatly contributed to addressing this issue. This brief proposes a contrastive learning training framework and an Inception module to extract multi-scale temporal and spatial features, representing the subject-invariant components of ERP signals. Specifically, a base encoder integrated with a linear Inception module and a nonlinear projector is used to project the raw data into latent space. By maximizing signal similarity under different targets, the inter-subject EEG signal differences in latent space are minimized. The extracted spatiotemporal features are then used for ERP target detection. The proposed algorithm achieved the best AUC performance in single-trial binary classification tasks on the P300 dataset and showed significant optimization in speller decoding tasks compared to existing algorithms.<|reference_end|>
@article{cui2024a,
    title={A Contrastive Learning Based Convolutional Neural Network for ERP
  Brain-Computer Interfaces},
    author={Yuntian Cui, Xinke Shen, Dan Zhang, Chen Yang},
    journal={arXiv preprint arXiv:2407.04738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04738},
    primaryClass={eess.SP cs.LG cs.RO}
}