The combination of CCA and PSDA detection methods in a SSVEP-BCI system

Ruimin Wang, Wen Wu, Keiji Iramina, Sheng Ge

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

4 被引用数 (Scopus)

抄録

In recent years, based on the steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) have generated significant interest, due to their shorter calibration times and higher information transfer rates. Target identification is the core signal processing task in BCIs. Power spectral density analysis (PSDA) and canonical correlation analysis (CCA) are the most popular and widely used classification methods in SSVEP-BCI systems. In this paper, we first combined these two methods for detecting the SSVEP signals. Moreover, we compared the proposed method with PSDA, CCA method, respectively. The results showed that the proposed method can improve the accuracy and the transfer rate of BCIs.

本文言語英語
ホスト出版物のタイトルProceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2424-2427
ページ数4
March
ISBN(電子版)9781479958252
DOI
出版ステータス出版済み - 3 2 2015
イベント2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, 中国
継続期間: 6 29 20147 4 2014

出版物シリーズ

名前Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
番号March
2015-March

その他

その他2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country中国
CityShenyang
Period6/29/147/4/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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