An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for fre...An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.展开更多
基金the National Natural Science Foundation of China(Nos.61702395 and 61972302)the Science and Technology Projects of Xi’an,China(No.201809170CX11JC12)。
文摘An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.
