A hybrid genetic algorithm approach for improving the performance of the LF-ASD brain computer interface

An asynchronous brain computer interface (BCI) continuously monitors the brain signals and is activated only when a user intends control. Initial results from an asynchronous system, the LF-ASD, designed by our group have shown promise, but the reported error rates are still high for most practical applications. To improve its performance, we propose user customization. Since energy normalization of all channels´ signals is shown to significantly improve the performance of the system, we choose to customize the parameters related to this normalization. We apply a hybrid genetic algorithm (a genetic algorithm followed by a local search) to customize the size of the energy normalization windows. This is shown to significantly improve the results. For a fixed false positive rate of 2%, the improvement in the true positive rate was raised from 65.7% to 76.9% in one subject and from 53.1% to 63.3% for another subject.