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

TitleApplying a Hybrid Genetic Algorithm in the Design of a Self-Paced Brain Interface with a Low False Positive Rate
Publication TypeConference Paper
Year of Publication2007
AuthorsFatourechi, M., G. E. Birch, and R. K. Ward
Conference NameAcoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
PaginationIV-1157 -IV-1160
Date Publishedapr.
KeywordsBeta rhythm, biocontrol, bioelectric potentials, electroencephalography, genetic algorithms, hybrid genetic algorithm, intentional control commands, low false positive rate, movement-related potentials, Mu rhythm, multiple classifier system, neurological phenomena, noisy background EEG signal, self-paced brain interface optimization, signal classification, user interfaces

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


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