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 clas

SUBMITTER: Bollens L 

PROVIDER: S-EPMC12329020 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature