A Kernel Canonical Correlation Analysis Based Idle-State Detection Method for SSVEP-Based Brain-Computer Interfaces

In this paper, we propose a kernel canonical correlation analysis (KCCA) based idle-state detection method for asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems.KCCA method can offer a flexible nonlinear solution to adequately extract nonlinear features of multi-electrode electroencephalogram signals.Based on this method, an ensemble KCCA coefficients feature model is proposed by weighting effectively multi-harmonic information and afterwards a threshold classification strategy for idle-state detection is presented.The weights of the model and optimal threshold are trained by the presented parameters learning scheme. Using our method, offline analysis was performed on 10 subjects with 8 fixed common electrodes.The results showed that the idle state could be detected with 95.9% average accuracy when SSVEP could be determined with 93.8% average accuracy.Further, the analysis verified the effectiveness and significant superiority of our method over other widely used ones.
kernel canonical correlation analysis (KCCA) idle-state detection ensemble feature model steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) electroencephalogram (EEG)
Zimu Zhang Zhidong Deng
State Key Laboratory of Intelligent Technology and Systems,Tsinghua National Laboratory for Information Science and Technology,Department of Computer Science and Technology,Tsinghua University,Beijing 100084 China
国际会议
厦门
英文
634-640
2011-07-08(万方平台首次上网日期,不代表论文的发表时间)