Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been one of the most stable BCI that is able to transfer commands effectively by the aids of canonical correlation analysis (CCA) for frequency recognition. Nevertheless, CCA-based SSVEP detection encounters the spectral non-uniformity of spontaneous background EEG activity which deteriorates the performance. In this study, we found the performance of SSVEP-based BCI highly predictable by the standard deviation of resting-state response using CCA. Therefore, the proposed normalized CCA (nCCA) aims to calibrate the spectral non-uniformity of background activity which causes bias in classification, and outperforms standard CCA significantly in simulated online tests. In addition, nCCA features near-zero calibration making it a nice fit to practical applications of SSVEP-based BCI spellers.

Original languageEnglish
Title of host publication2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728190488
DOIs
StatePublished - 2021
Event2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States
Duration: 5 Dec 20217 Dec 2021

Publication series

Name2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

Conference

Conference2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Country/TerritoryUnited States
CityOrlando
Period5/12/217/12/21

Keywords

  • Brain-computer interface (BCI)
  • Canonical correlation analysis (CCA)
  • Electroencephalography (EEG)
  • Steady-state evoked potential (SSVEP)

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