TY - GEN
T1 - Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection
AU - Wei, Chun Shu
N1 - Publisher Copyright:
© 2021 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85125814156&partnerID=8YFLogxK
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 -