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• W2059653419 abstract "Canonical correlation analysis (CCA) has been successfully applied to steady-state visual evoked potential (SSVEP) recognition for brain–computer interface (BCI) application. Although the CCA method outperforms the traditional power spectral density analysis through multi-channel detection, it requires additionally pre-constructed reference signals of sine–cosine waves. It is likely to encounter overfitting in using a short time window since the reference signals include no features from training data. We consider that a group of electroencephalogram (EEG) data trials recorded at a certain stimulus frequency on a same subject should share some common features that may bear the real SSVEP characteristics. This study therefore proposes a common feature analysis (CFA)-based method to exploit the latent common features as natural reference signals in using correlation analysis for SSVEP recognition. Good performance of the CFA method for SSVEP recognition is validated with EEG data recorded from ten healthy subjects, in contrast to CCA and a multiway extension of CCA (MCCA). Experimental results indicate that the CFA method significantly outperformed the CCA and the MCCA methods for SSVEP recognition in using a short time window (i.e., less than 1 s). The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI." @default.
• W2059653419 created "2016-06-24" @default.
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• W2059653419 date "2015-04-01" @default.
• W2059653419 modified "2023-10-16" @default.
• W2059653419 title "SSVEP recognition using common feature analysis in brain–computer interface" @default.
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• W2059653419 doi "https://doi.org/10.1016/j.jneumeth.2014.03.012" @default.
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