TY - GEN
T1 - Decoding Imagined, Heard, and Spoken Speech: Classification and Regression of EEG Using a 14-Channel Dry-Contact Mobile Headset
AU - Clayton, Jonathan
AU - Wellington, Scott
AU - Valentini-Botinhao, Cassia
AU - Watts, Oliver
PY - 2020/10/25
Y1 - 2020/10/25
N2 - 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.
AB - 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.
UR - http://dx.doi.org/10.21437/interspeech.2020-2745
U2 - 10.21437/interspeech.2020-2745
DO - 10.21437/interspeech.2020-2745
M3 - Chapter in a published conference proceeding
SP - 4886
EP - 4890
BT - Interspeech 2020
ER -