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

Ruimin Wang, Wen Wu, Keiji Iramina, Sheng Ge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2424-2427
Number of pages4
EditionMarch
ISBN (Electronic)9781479958252
DOIs
Publication statusPublished - Mar 2 2015
Event2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China
Duration: Jun 29 2014Jul 4 2014

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
NumberMarch
Volume2015-March

Other

Other2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/TerritoryChina
CityShenyang
Period6/29/147/4/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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