Sparse Bayesian multiway canonical correlation analysis for EEG pattern recognition

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• 作者：Yu Zhang^a ; ^{yuzhang@ecust.edu.cn}Author Vitae ; Guoxu Zhou^bAuthor Vitae ; Jing Jin^aAuthor Vitae ; Yangsong Zhang^cAuthor Vitae ; Xingyu Wang^aAuthor Vitae ; Andrzej Cichocki^dAuthor Vitae
• 关键词：Brain&ndash ; computer interface (BCI) ; Electroencephalogram (EEG) ; Multiway canonical correlation analysis (MCCA) ; Sparse Bayesian learning ; Steady-state visual evoked potential (SSVEP)
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：225
• 期：Complete
• 页码：103-110
• 全文大小：773 K
• 卷排序：225

文摘

L1-regularized multiway canonical correlation analysis (L1-MCCA) has been introduced to reference signal optimization in steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI). The effectiveness of L1-regularization on significant trial selection highly depends on the regularization parameter setting, which can be typically determined by cross-validation (CV). However, CV will substantially reduce the practicability of BCI system due to additional data requirement for the parameter validation and relatively high computational cost. To solve the problem, this study proposes a Bayesian version of L1-MCCA (called SBMCCA) by exploiting sparse Bayesian learning. The SBMCCA method avoids CV and can efficiently estimate the model parameters under the Bayesian evidence framework. Experimental results show that the SBMCCA method achieved comparable recognition accuracy but much higher computational efficiency in contrast to the L1-MCCA method.