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

Chun Shu Wei

doi:10.1109/SSCI50451.2021.9660155

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

Chun Shu Wei

研究成果: Conference contribution › 同行評審

摘要

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.

原文	English
主出版物標題	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781728190488
DOIs	https://doi.org/10.1109/SSCI50451.2021.9660155
出版狀態	Published - 2021
事件	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, 美國持續時間: 5 12月 2021 → 7 12月 2021

出版系列

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

Conference

Conference	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
國家/地區	美國
城市	Orlando
期間	5/12/21 → 7/12/21

UN SDG

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存取文件

10.1109/SSCI50451.2021.9660155

其它文件與鏈接

Link to publication in Scopus

引用此

Wei, C. S. (2021). Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection. 於 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI50451.2021.9660155

@inproceedings{0f89bdd0f9e949f18742960f87593a1f,

title = "Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection",

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.",

keywords = "Brain-computer interface (BCI), Canonical correlation analysis (CCA), Electroencephalography (EEG), Steady-state evoked potential (SSVEP)",

author = "Wei, \{Chun Shu\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 ; Conference date: 05-12-2021 Through 07-12-2021",

year = "2021",

doi = "10.1109/SSCI50451.2021.9660155",

language = "English",

series = "2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings",

address = "美國",

}

Wei, CS 2021, Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection. 於 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, Orlando, 美國, 5/12/21. https://doi.org/10.1109/SSCI50451.2021.9660155

Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection. / Wei, Chun Shu.
2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings).

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection

AU - Wei, Chun Shu

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Brain-computer interface (BCI)

KW - Canonical correlation analysis (CCA)

KW - Electroencephalography (EEG)

KW - Steady-state evoked potential (SSVEP)

UR - https://www.scopus.com/pages/publications/85125814156

U2 - 10.1109/SSCI50451.2021.9660155

DO - 10.1109/SSCI50451.2021.9660155

M3 - Conference contribution

AN - SCOPUS:85125814156

T3 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021

Y2 - 5 December 2021 through 7 December 2021

ER -

Wei CS. Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection. 於 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings). doi: 10.1109/SSCI50451.2021.9660155

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

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出版系列

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UN SDG

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