A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces
This addresses the problem of low signal-to-noise ratio and inter-subject variability in ERP-based EEG detection for brain-computer interface applications, representing an incremental improvement.
The paper tackled cross-subject ERP signal detection in brain-computer interfaces by proposing a contrastive learning framework with an Inception module, achieving the best AUC performance in single-trial binary classification on the P300 dataset and significant optimization in speller decoding tasks.
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.