Decoding imagined, heard, and spoken speech: Classification and regression of EEG using a 14-channel dry-contact mobile headset

Jonathan Clayton, Scott Wellington, Cassia Valentini-Botinhao, Oliver Watts

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.

Original languageEnglish
Title of host publicationProceedings Interspeech 2020
PublisherInternational Speech Communication Association
Pages4886-4890
Number of pages5
Volume2020-October
DOIs
Publication statusPublished - 25 Oct 2020
EventInterspeech 2020 - Virtual Conference, China
Duration: 25 Oct 202029 Oct 2020
http://www.interspeech2020.org/

Publication series

Name
PublisherInternational Speech Communication Association
ISSN (Electronic)1990-9772

Conference

ConferenceInterspeech 2020
Abbreviated titleINTERSPEECH 2020
Country/TerritoryChina
CityVirtual Conference
Period25/10/2029/10/20
Internet address

Keywords / Materials (for Non-textual outputs)

  • Brain-computer interfaces
  • EEG
  • Imagined speech
  • Neural decoding
  • Stimulus reconstruction

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