Optimizing SSVEP-based brain-computer interface with CCA and Genetic Algorithms

Abstract

Brain-computer interface (BCI) is used for non-muscular communication between the human brain and an external device. One of the most commonly utilized phenomena in BCI are steady-state visually evoked potentials (SSVEP) that are measured with electroencephalography (EEG). If the subject focuses his/her attention on the stimulus flashing with specified frequency on the computer screen, a signal fluctuations with the same frequency may be observed in his or her visual cortex. Canonical Correlation Analysis (CCA) was adapted to EEG signal analysis and the outcomes of this straightforward approach are more promising in terms of obtaining decoding accuracy than conventional methods. In CCA two sets of data are being compared: a multichannel signal, and a reference prior corresponding to the classes between which the distinction is being made. Category of the signal (e.g., 8 Hz vs 14 Hz) is determined on the basis of the correlation coefficient between the acquired signal and several reference signals. The aim of this paper is to present an idea of utilizing genetic algorithms to generate these reference priors. Outcomes of our analyses suggest that a genetic approach (with signal encoding in the form of the binary code) and tournament selection method may improve the accuracy of SSVEP classification. Further work may help to improve the accuracy even more, with different encoding scheme or alternative and/or more sophisticated selection approach.