Steady-state visual evoked potential(SSVEP)has become a powerful tool for Brain Computer Interface(BCI)because of its high signal-tonoise ratio,high information transmission rate,and minimal user training.At present,t...Steady-state visual evoked potential(SSVEP)has become a powerful tool for Brain Computer Interface(BCI)because of its high signal-tonoise ratio,high information transmission rate,and minimal user training.At present,the edge information of each region cannot be identified in spatial coding based on SSVEP-BCI technology,and the user experience is poor.To solve this problem,this paper designed a new paradigm to explore the relationship between the fixation point position of continuous sliding and the correlation coefficient ratio in the dualfrequency case.Firstly,the standard sinusoidal signal was employed to simulate the Electroencephalogram(EEG)signal,which verified the reliability of characterizing the amplitude variation of test signal by correlation coefficient.Then,the relationship between the amplitude response of SSVEP and the distance between the fixation point and the stimulus in the horizontal direction was tested by Canonical Correlation Analysis(CCA)and Filter bank CCA(FBCCA).Finally,the experimental data were offline analyzed under the condition of continuous sliding of the fixation point.It is feasible and reasonable to detect the amplitude change of frequency component in SSVEP by utilizing the spatial coding method in this paper to improve the extraction accuracy of spatial information.展开更多
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
文摘Steady-state visual evoked potential(SSVEP)has become a powerful tool for Brain Computer Interface(BCI)because of its high signal-tonoise ratio,high information transmission rate,and minimal user training.At present,the edge information of each region cannot be identified in spatial coding based on SSVEP-BCI technology,and the user experience is poor.To solve this problem,this paper designed a new paradigm to explore the relationship between the fixation point position of continuous sliding and the correlation coefficient ratio in the dualfrequency case.Firstly,the standard sinusoidal signal was employed to simulate the Electroencephalogram(EEG)signal,which verified the reliability of characterizing the amplitude variation of test signal by correlation coefficient.Then,the relationship between the amplitude response of SSVEP and the distance between the fixation point and the stimulus in the horizontal direction was tested by Canonical Correlation Analysis(CCA)and Filter bank CCA(FBCCA).Finally,the experimental data were offline analyzed under the condition of continuous sliding of the fixation point.It is feasible and reasonable to detect the amplitude change of frequency component in SSVEP by utilizing the spatial coding method in this paper to improve the extraction accuracy of spatial information.
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.
