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2020 INTERSPEECH INTERSPEECH 2020

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

Abstract

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
📈 Trend Setter — Brain-Computer Interface
🧭 Keyword Pioneer — mobile eeg device
🐣 Hot Topic Early Bird — brain-computer interface
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Artificial Intelligence > Core AI > Multimodal Learning Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Regression Artificial Intelligence > Core AI > Brain-Computer Interface

Keywords

eeg classification brain-computer interface speech decoding eeg signal spectral feature imagined speech mobile eeg device
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