Decoding imagined speech from EEG signals using hybrid-scale spatial-temporal dilated convolution network.

Directly decoding imagined speech from electroencephalogram (EEG) signals has attracted much interest in brain-computer interface applications, because it provides a natural and intuitive communication method for locked-in patients. Several methods have been applied to imagined speech decoding, but how to construct spatial-temporal dependencies and capture long-range contextual cues in EEG signals to better decode imagined speech should be considered.In this study, we propose a novel model called hybrid-scale spatial-temporal dilated convolution network (HS-STDCN) for EEG-based imagined speech recognition.