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

Pan，Changjie1; Lai，Ying Hui2; Chen，Fei1

10.1109/EMBC46164.2021.9629709

2021

43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

1557-170X

1558-4615

978-1-7281-1180-3

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

131-134

NOV 01-05, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

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.

Electrodes Manifolds Electric potential Frequency-domain analysis Scalp Electroencephalography Brain-computer interfaces

第一

英语

CPCI-S ; EI

Shenzhen Sustainable Support Program for High-level University[20200925154002001]

Engineering

Engineering, Biomedical ; Engineering, Electrical & Electronic

WOS:000760910500030

20220811670391

2-s2.0-85122518683

Scopus

1.Southern University of Science and Technology,Department of Electrical and Electronic Engineering,Shenzhen,China
2.Department of Biomedical Engineering,National Yang Ming Chiao Tung University,Chinese Taipei,Taiwan

Pan，Changjie,Lai，Ying Hui,Chen，Fei. The Effects of Classification Method and Electrode Configuration on EEG-based Silent Speech Classification[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:131-134.