VEP-based brain-computer interfaces modulated by Golay complementary series for improving performance.

BACKGROUND The goal of a brain-computer interface (BCI) is to enable communication by pure brain activity without neural and muscle control. However, the practical use of BCIs is limited by low information transfer rate. Recently, code modulation visual evoked potential (c-VEP) based BCIs have exhibited great potential in establishing high-rate communication between the brain and the external world. OBJECTIVE This study aims at exploring a more effective modulation code than the commonly used pseudorandom M sequence for c-VEP based BCIs (c-VEP BCIs) in order to increase the detection accuracy of stimulus targets and the resulting information transfer rate. METHOD Golay complementary sequence pair is used for constructing the modulation code of c-VEP BCIs due to their superior autocorrelation property. The modulation code is created by concatenating a pair of Golay complementary sequences. Sixteen target stimuli are modulated by the Golay code and its time shift versions. RESULTS Through offline analysis on data recorded from seven subjects and online test on five subjects, the Golay code modulated BCI yielded higher detection accuracy and information transfer rate than those achieved by M sequence. CONCLUSION The Golay code modulated BCI demonstrates a high performance compared with the M sequence modulated systems, and it is applicable to persons with motor disabilities.

[1]  H. A. Barker,et al.  Effects of nonlinearities on the measurement of weighting functions by crosscorrelation using pseudorandom signals , 1973 .

[2]  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.

[3]  V. Braun Golay sequences for identification of linear systems with weak nonlinear distortion , 1998 .

[4]  Christopher Warren,et al.  Impulse response measurement using complementary sequences , 2014 .

[5]  E.S. Ebbini,et al.  A new coded-excitation ultrasound imaging system. I. Basic principles , 1996, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[6]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[7]  Erich E. Sutter,et al.  The brain response interface: communication through visually-induced electrical brain responses , 1992 .

[8]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[9]  Marcel J. E. Golay,et al.  Complementary series , 1961, IRE Trans. Inf. Theory.

[10]  Y Takeuchi,et al.  An investigation of a spread energy method for medical ultrasound systems. Part one: theory and investigation. , 1979, Ultrasonics.

[11]  Xiaorong Gao,et al.  An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method , 2009, Journal of neural engineering.

[12]  Yijun Wang,et al.  A high-speed BCI based on code modulation VEP , 2011, Journal of neural engineering.