Online Recursive ICA Algorithm Used for Motor Imagery EEG Signal

Xueyi Lin, Lu Wang, Tomoaki Ohtsuki

研究成果: Conference contribution

抄録

Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA's adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.

本文言語English
ホスト出版物のタイトル42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
ホスト出版物のサブタイトルEnabling Innovative Technologies for Global Healthcare, EMBC 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ502-505
ページ数4
ISBN(電子版)9781728119908
DOI
出版ステータスPublished - 2020 7
イベント42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
継続期間: 2020 7 202020 7 24

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2020-July
ISSN(印刷版)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period20/7/2020/7/24

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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