Decoding Imagined, Heard, and Spoken Speech: Classification and Regression of EEG Using a 14-Channel Dry-Contact Mobile Headset

We investigate the use of a 14-channel, mobile EEG device in the decoding of heard, imagined, and articulated English phones from brainwave data. To this end we introduce a dataset that fills a current gap in the range of available open-access EEG datasets for speech processing with lightweight, affordable EEG devices made for the consumer market. We investigate the effectiveness of two classification models and a regression model for reconstructing spectral features of the original speech signal. We report that our classification performance is almost on a par with similar findings that use EEG data collected with research-grade devices. We conclude that commercial-grade devices can be used as speech-decoding BCIs with minimal signal processing.