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

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
出版狀態Published - 2021
事件2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States
持續時間: 5 12月 20217 12月 2021

出版系列

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

Conference

Conference2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
國家/地區United States
城市Orlando
期間5/12/217/12/21

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