FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS

• Published in: International journal of neural systems 2014-06, Vol.24 (4), p.1450013
• Main Authors: ZHANG, YU, ZHOU, GUOXU, JIN, JING, WANG, XINGYU, CICHOCKI, ANDRZEJ
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Applied sciences; Biological and medical sciences; Brain; Brain - physiology; Brain Mapping; Brain-Computer Interfaces; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Correlation analysis; Data processing. List processing. Character string processing; Electrodiagnosis. Electric activity recording; Electroencephalography; Evoked Potentials, Visual - physiology; Exact sciences and technology; Eye and associated structures. Visual pathways and centers. Vision; Feature extraction; Female; Fundamental and applied biological sciences. Psychology; Humans; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Memory organisation. Data processing; Nervous system; Optimization; Pattern Recognition, Automated; Photic Stimulation; Recognition (Psychology); Reference signals; Software; Spatial filtering; Trigonometric functions; User-Computer Interface; Vertebrates: nervous system and sense organs; Windows (intervals); Young Adult
• ISSN: 0129-0657; 1793-6462
• DOI: 10.1142/S0129065714500130

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.