An improved multiple LASSO model for steady-state visual evoked potential detection

Ruimin Wang, Keiji Iramina, Sheng Ge

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Improving the classification accuracy in brain–computer interface (BCI) with a short data length is important to increase the BCI system’s information transfer rate. Least absolute shrinkage and selection operator (LASSO) has been examined to be an effective way to detect the steady-state visual evoked potential (SSVEP) signals with a short time window. In this paper, an improved multiple LASSO model for SSVEP detection is proposed, which can process multichannel electroencephalogram (EEG) signals without electrode selection. EEG data from twelve healthy volunteers were used to test the improved multiple LASSO model. Compared with the traditional LASSO model, the improved multiple LASSO model gives a significantly better performance with multichannel EEG data.

本文言語英語
ホスト出版物のタイトル6th International Conference on the Development of Biomedical Engineering in Vietnam, BME6
編集者Toi Vo Van, Thanh An Nguyen Le, Thang Nguyen Duc
出版社Springer Verlag
ページ427-430
ページ数4
ISBN(印刷版)9789811043604
DOI
出版ステータス出版済み - 1 1 2018
イベント6th International Conference on the Development of Biomedical Engineering in Vietnam, BME 2016 - Ho Chi Minh, ベトナム
継続期間: 6 27 20166 29 2016

出版物シリーズ

名前IFMBE Proceedings
63
ISSN(印刷版)1680-0737

その他

その他6th International Conference on the Development of Biomedical Engineering in Vietnam, BME 2016
Countryベトナム
CityHo Chi Minh
Period6/27/166/29/16

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biomedical Engineering

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