Discrimination between idle and work states in BCI based on SSVEP

We present a novel method for idle and work states classification in brain computer interface (BCI) based on steady-state visual evoked potentials (SSVEP). Canonical correlation analysis (CCA) and maximum contrast combination (MCC) are used to extract features of electroencephalogram (EEG) signals. The correlation coefficients from CCA and SNR from MCC were classified by a linear classifier. Then an extra strategy of excluding alpha wave interference helped improve the classification accuracy. This method had a good performance in real EEG signals.

[1]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  M. Kupersmith.  Human Brain Electrophysiology , 1989 .

[3]  Francisco Sepulveda,et al.  Mental task classification against the idle state: A preliminary investigation , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Xiaorong Gao,et al.  Idle State Detection in SSVEP-Based Brain-Computer Interfaces , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical 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]  Wei Wu,et al.  Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs , 2007, IEEE Transactions on Biomedical Engineering.

[7]  Michael S. Lazar,et al.  Spatial patterns underlying population differences in the background EEG , 2005, Brain Topography.

[8]  Christa Neuper,et al.  An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate , 2004, IEEE Transactions on Biomedical Engineering.

[9]  Gary E. Birch,et al.  A brain-controlled switch for asynchronous control applications , 2000, IEEE Trans. Biomed. Eng..

[10]  C. Binnie,et al.  A glossary of terms most commonly used by clinical electroencephalographers. , 1974, Electroencephalography and clinical neurophysiology.

[11]  Ivan Volosyak,et al.  Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.