A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces

TL;DR

This paper introduces a contrastive learning framework with an Inception-based CNN to improve cross-subject ERP detection in EEG signals, achieving state-of-the-art results in classification and speller decoding tasks.

Contribution

It proposes a novel contrastive learning approach combined with multi-scale feature extraction for ERP detection, enhancing inter-subject invariance and performance.

Findings

Achieved highest AUC in single-trial P300 classification

Significantly improved speller decoding accuracy

Outperformed existing algorithms in ERP detection tasks

Abstract

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.