Applying a Hybrid Genetic Algorithm in the Design of a Self-Paced Brain Interface with a Low False Positive Rate

Abstract

A new hybrid genetic algorithm (HGA) for optimization of a self-paced brain interface (SBI) is proposed. To identify intentional control (IC) commands in the noisy background EEG signal, the proposed SBI uses features extracted from three neurological phenomena - movement-related potentials as well as changes in the power of Mu and Beta rhythms. To identify the IC commands, for each neurological phenomenon, a multiple classifier system (MCS) is designed. Then a 2nd-stage MCS combines the outputs of the individual MCSs and generates the final decision. The HGA selects the optimal subset of features, the optimal parameter values of the classifiers, as well as the best configuration for combining the MCSs. Analysis of the data of four subjects shows an average TP = 56.18%, and an average FP = 0.14%, a significant improvement over our previous SBI design.