A Study on SSVEP-Based BCI

Brain-computer interface (BCI) can help the deformity person finish some basic activities. In this paper, we concern some critical aspects of SSVEP based BCI, including stimulator selection, method of SSVEP extracting in a short time, stimulating frequency selection, and signal electrode selection. The conclusion is that the stimulator type should be based on the complexity of the BCI system, the method based on wavelet analysis is more valid than the power spectrum method in extracting the SSVEP in a short period, and the selections of stimulating frequency and electrode are important in designing a BCI system. These contents are meaningful for implementing a real SSVEP-based BCI.

[1]  C. Herrmann.  Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.

[2]  Xiaorong Gao,et al.  Design and implementation of a brain-computer interface with high transfer rates , 2002, IEEE Transactions on Biomedical Engineering.

[3]  Wei Wu,et al.  Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs , 2006, IEEE Transactions on Biomedical Engineering.

[4]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  G Calhoun,et al.  Brain-computer interfaces based on the steady-state visual-evoked response. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  Dezhong Yao,et al.  Stimulator selection in SSVEP-based BCI. , 2008, Medical engineering & physics.

[7]  Dezhong Yao,et al.  Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs. , 2008, Journal of neural engineering.

[8]  Gao Xiaorong,et al.  Stimulation frequency extraction in SSVEP-based brain-computer interface , 2005, Proceedings. 2005 First International Conference on Neural Interface and Control, 2005..

[9]  Yijun Wang,et al.  Lead selection for SSVEP-based brain-computer interface , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Reinhold Scherer,et al.  Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components , 2005, Journal of neural engineering.

[11]  W. Wong,et al.  Time-frequency analysis of visual evoked potentials by means of matching pursuit with chirplet atoms , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Yao Dezhong,et al.  Nonlinear Analysis of Clinical Epileptic EEG by Approximate Entropy , 2005 .

[13]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[14]  Gao Xiaorong,et al.  Lead selection for SSVEP-based binocular rivalry , 2005, Proceedings. 2005 First International Conference on Neural Interface and Control, 2005..

[15]  F. Piccione,et al.  P300-based brain computer interface: Reliability and performance in healthy and paralysed participants , 2006, Clinical Neurophysiology.