title: An efficient frequency recognition method based on likelihood ratio test for SSVEP-based BCI.
abstract: An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI.
authors.affiliation: School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China.
authors.forename: Yangsong
authors.identifier: 0000-0002-6764-3567
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Li
authors.initials: L
authors.lastname: Dong
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
authors.affiliation: Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
mesh_terms: D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces / Q000523:psychology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D010775:Photic Stimulation; D055815:Young Adult