Reject option to reduce false prediction rates for EEG-motor imagery based BCI

The Filter Bank Common Spatial Pattern (FBCSP) algorithm had been shown to be effective in performing multi-class Electroencephalogram (EEG) decoding of motor imagery using the one-versus-the-rest approach on the BCI Competition IV Dataset IIa. In this paper, we propose a method to reduce false detection rates of decoding through a rejection option based on the difference in the posterior probability computed by the Naïve Bayesian classifier. We applied the proposed approach on the BCI Competition IV Dataset IIa, and the results showed a decrease in the false detection rates from 34.6 % to 6.9%, while average decoded trials decreased from 93.2% to 34.2% using a rejection threshold between 0.1 and 0.9. We subsequently formulated a method to optimize the rejection threshold based on the maximum F_0.5 score. The optimal rejection threshold yielded an average decrease in false detection rate to 19.1% with an average of 67.5% of trials decoded. The results showed the feasibility of decreasing false detection rates at a cost of rejection. Nevertheless, the results suggest that the use of reject option (RO) may be used as a training feedback system to train subjects' overt and covert EEG control strategies for better (dexterity and safety) continuous control of external device.