ADT Network: A Novel Nonlinear Method for Decoding Speech Envelopes From EEG Signals.

ABSTRACT: Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods struggle to meet both high accuracy and interpretability. We propose a deep learning model called the auditory decoding transformer (ADT) network for speech envelope reconstruction from EEG signals to address these issues. The ADT network uses spatio-temporal convolution for feature extraction, followed by a transformer decoder to decode the speech envelopes. Through anticausal masking, the ADT considers only the current and future EEG features to match the natural relationship of speech and EEG. Performance evaluation shows that the ADT network ac

SUBMITTER: Liu R 

PROVIDER: S-EPMC11489951 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature