title: A MUSIC-based method for SSVEP signal processing.
abstract: The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.
authors.affiliation: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China.|Key Laboratory of Fiber Optic Sensing Technology and Information Processing,  Ministry of Education, Wuhan, 430070, China.
authors.forename: Kun
authors.initials: K
authors.lastname: Chen
authors.affiliation: School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China. kunchen@whut.edu.cn.|Key Laboratory of Fiber Optic Sensing Technology and Information Processing,  Ministry of Education, Wuhan, 430070, China. kunchen@whut.edu.cn.
authors.forename: Quan
authors.initials: Q
authors.lastname: Liu
authors.affiliation: School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China.|Key Laboratory of Fiber Optic Sensing Technology and Information Processing,  Ministry of Education, Wuhan, 430070, China.
authors.forename: Qingsong
authors.initials: Q
authors.lastname: Ai
authors.affiliation: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China.|Key Laboratory of Fiber Optic Sensing Technology and Information Processing,  Ministry of Education, Wuhan, 430070, China.
authors.forename: Zude
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: Department of Mechanical Engineering, The University of Auckland, Auckland, 1010, New Zealand.
authors.forename: Sheng Quan
authors.initials: SQ
authors.lastname: Xie
authors.affiliation: School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China.|Key Laboratory of Fiber Optic Sensing Technology and Information Processing,  Ministry of Education, Wuhan, 430070, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Meng
mesh_terms: D000465:Algorithms; D000669:Amplifiers, Electronic; D003198:Computer Simulation; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D010775:Photic Stimulation; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic; D013997:Time Factors
keywords: Brain computer interface (BCI); Feature extraction; Multiple signal classification (MUSIC); Steady state visual evoked potential (SSVEP)