The Effects of Classification Method and Electrode Configuration on EEG-based Silent Speech Classification

Changjie Pan, Ying Hui Lai, Fei Chen

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

The effective classification for imagined speech and intended speech is of great help to the development of speech-based brain-computer interfaces (BCIs). This work distinguished imagined speech and intended speech by employing the cortical EEG signals recorded from scalp. EEG signals from eleven subjects were recorded when they produced Mandarin-Chinese monosyllables in imagined speech and intended speech, and EEG features were classified by the common spatial pattern, time-domain, frequency-domain and Riemannian manifold based methods. The classification results indicated that the Riemannian manifold based method yielded the highest classification accuracy of 85.9% among the four classification methods. Moreover, the classification accuracy with the left-only brain electrode configuration was close to that with the whole brain electrode configuration. The findings of this work have potential to extend the output commands of silent speech interfaces.

原文English
主出版物標題43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面131-134
頁數4
ISBN(電子)9781728111797
DOIs
出版狀態Published - 2021
事件43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico
持續時間: 1 11月 20215 11月 2021

出版系列

名字Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(列印)1557-170X

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
國家/地區Mexico
城市Virtual, Online
期間1/11/215/11/21

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