  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.
