Abstract
We have developed an unsupervised multi-class classification algorithm of Steady-State Visual Evoked Potential (SSVEP)-based-BCI. Our previously proposed algorithm detects the frequency that gives maximum peak-to-baseline amplitude of the spectrum power among the frequency bands corresponding to the flicker light stimuli at the three electrodes arranged in occipital area, and majority voting among these electrodes determines the final output. To further increase the accuracy, we examined whether (1) a feature that incorporates the peak-to-baseline amplitude of the spectrum power at the harmonic frequencies of the flicker frequency in addition to those at the fundamental frequency, and/or (2) a winner-take-all fashion of classification algorithm (classification based on the feature at a single electrode showing the maximum peak-to-baseline amplitude among three electrodes), could improve the accuracy. Mean accuracy (N=32) obtained from the classification algorithm using spectrum features at first and second harmonics in a winner-take-all selection strategy was 79.2%, which is significantly better than the accuracy from our previous classification algorithm (68.8%, p<0.001)
