Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer
This work addresses the problem of understanding and decoding covert speech from EEG for applications in brain-computer interfaces, representing an incremental advancement with a novel method for a known bottleneck.
The study tackled the challenge of decoding covert speech from EEG signals by developing a Functional Areas Spatio-temporal Transformer (FAST) framework, which revealed distinct and interpretable neural features across brain regions, providing new insights into neural representation.
Covert speech involves imagining speaking without audible sound or any movements. Decoding covert speech from electroencephalogram (EEG) is challenging due to a limited understanding of neural pronunciation mapping and the low signal-to-noise ratio of the signal. In this study, we developed a large-scale multi-utterance speech EEG dataset from 57 right-handed native English-speaking subjects, each performing covert and overt speech tasks by repeating the same word in five utterances within a ten-second duration. Given the spatio-temporal nature of the neural activation process during speech pronunciation, we developed a Functional Areas Spatio-temporal Transformer (FAST), an effective framework for converting EEG signals into tokens and utilizing transformer architecture for sequence encoding. Our results reveal distinct and interpretable speech neural features by the visualization of FAST-generated activation maps across frontal and temporal brain regions with each word being covertly spoken, providing new insights into the discriminative features of the neural representation of covert speech. This is the first report of such a study, which provides interpretable evidence for speech decoding from EEG. The code for this work has been made public at https://github.com/Jiang-Muyun/FAST