SSVEP recognition using common feature analysis in brain–computer interface

• Published in: Journal of neuroscience methods Vol. 244; pp. 8 - 15
• Main Authors: Zhang, Yu, Zhou, Guoxu, Jin, Jing, Wang, Xingyu, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.04.2015
• Subjects: Adult; Algorithms; Brain - physiology; Brain Mapping; Brain-Computer Interfaces; Brain–computer interface (BCI); Canonical correlation analysis (CCA); Common feature analysis (CFA); Electroencephalogram (EEG); Electroencephalography; Evoked Potentials, Visual - physiology; Humans; Male; Pattern Recognition, Automated; Photic Stimulation; Steady-state visual evoked potential (SSVEP); Young Adult; Common feature analysis (CFA); Steady-state visual evoked potential (SSVEP); Electroencephalogram (EEG); Canonical correlation analysis (CCA); Brain–computer interface (BCI)
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2014.03.012

•We propose a common feature analysis method to exploit the common features from EEG as references for SSVEP recognition.•EEG data recorded from ten healthy subjects are used to validate effectiveness of the CFA method for SSVEP recognition.•Experimental results indicate that CFA significantly outperformed CCA and MCCA methods in using a short time window.•Our study confirms the proposed CFA method is promising for the development of a high-speed SSVEP-based BCI. 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 1s). The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI.