An Approach to a Phase Model for Steady State Visually Evoked Potentials
One of the most used signals in Brain Machine Interfaces (BMI) is the Steady State Visually Evoked Potentials (SSVEP). In a SSVEP-based BMI, a visual stimulus that flickers in a constant frequency is presented to the user, and the system has to detect if the user is gazing the stimulus. Usually the stimulus is a rectangular signal and there are no clear criteria for select the duty cycle, which is generally fixed to 50 %. We propose a model for SSVEP that links the phase and amplitude variations in function of the duty cycle for a specific frequency. This model can be adjusted using only the phase of the SSVEP signal and it could improve the SSVEP-based BMI by selecting the duty cycle. The model was fixed for SSVEP responses in a man who is 39 years old. The mean absolute error below 0.3 rad shows that the model predicts the phase in the majority of the used frequencies.
This work is funded by the Government of Switzerland through the call for Seed Money Grants Latin America 2015, and is supported by the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and the Universidad del Valle in Colombia; it is also supported by CYTED REASISTE network.
- 1.J.R. Wolpaw, N. Birbaumer, W.J. Heetderks, D.J. MacFarland, P.H. Peckham, G. Schalk, E. Donchin, L.A. Quatrano, C.J. Robinson, T.M. Vaughan, Brain-computer interface technology: a review of the first international meeting. IEEE Trans. Rehabil. Eng. 8(2) (2000)Google Scholar
- 2.S. Qiu, Z. Li, W. He, L. Zhang, C. Yang, C. Su, Teleoperation control of an exoskeleton robot using brain machine interface and visual compressive sensing. IEEE Trans. Fuzzy Syst. 99 (2016). IEEE Early Access ArticlesGoogle Scholar
- 5.G.R. Burkitta, R.B. Silbersteina, P.J. Caduschb, A.W. Wood, Steady-state visual evoked potentials and travelling waves. Clin. Neurophysiol. 111(2) (2000)Google Scholar
- 6.R. Cunnington, K.B. Ng, A.P. Bradley, Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interface using component synchrony measure, in WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane, Australia, 10–15 June 2012Google Scholar