Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis

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...

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Vydané v:International journal of neural systems Ročník 24; číslo 4; s. 1450013
Hlavní autori: Zhang, Yu, Zhou, Guoxu, Jin, Jing, Wang, Xingyu, Cichocki, Andrzej
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore 01.06.2014
Predmet:
Adult
Algorithms
Brain - physiology
Brain Mapping
Brain-Computer Interfaces
Electroencephalography
Evoked Potentials, Visual - physiology
Female
Humans
Male
Pattern Recognition, Automated
Photic Stimulation
Recognition (Psychology)
User-Computer Interface
Young Adult
ISSN:0129-0657, 1793-6462, 1793-6462
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0129-0657
1793-6462
1793-6462
DOI:10.1142/S0129065714500130