Contrastive representation learning with transformers for robust auditory EEG decoding

Abstract

Decoding of continuous speech from electroencephalography (EEG) presents a promising avenue for understanding neural mechanisms of auditory processing and developing applications in hearing diagnostics. Recent advances in deep learning have improved decoding accuracy. However, challenges remain due to the low signal-to-noise ratio of the recorded brain signals. This study explores the application of contrastive learning, a self-supervised learning technique, to learn robust latent representations of EEG signals. We introduce a novel model architecture that leverages contrastive learning and transformer networks to capture relationships between auditory stimuli and EEG responses. Our model is evaluated on two tasks from the ICASSP 2023 Auditory EEG Decoding Challenge: a binary stimulus classification task (match-mismatch) and stimulus envelope decoding. We achieve state-of-the-art performance on both tasks, significantly outperforming previous winners with 87% accuracy in match-mismatch classification and a 0.176 Pearson correlation in envelope regression. Furthermore, we investigate the impact of model architecture, training set size, and finetuning on decoding performance, providing insights into the factors influencing model generalizability and accuracy. Our findings underscore the potential of contrastive learning for advancing the field of auditory EEG decoding and its potential applications in clinical settings.