title: Genetic feature selection to optimally detect P300 in brain computer interfaces.
abstract: A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration for feature selection and classification. The original input patterns were provided by two channels (Oz and Fz) of resampled EEG registers and wavelet coefficients. To evaluate the performance of the system, accuracy, sensibility and specificity were calculated. The wrapped wavelet patterns show a better performance than the temporal ones. The results were similar for patterns from channel Oz and Fz, together or separated.
authors.affiliation: Laboratorio de Ingeniería en Rehabilitación e Investigaciones Neuromusculares y Sensoriales, Facultad de Ingeniería, Universidad Nacional de Entre Ríos, CC 47 suc.3, CP 3100 Paraná, Argentina.
authors.forename: Yanina
authors.initials: Y
authors.lastname: Atum
authors.forename: Iván
authors.initials: I
authors.lastname: Gareis
authors.forename: Gerardo
authors.initials: G
authors.lastname: Gentiletti
authors.forename: Rubén
authors.initials: R
authors.lastname: Acevedo
authors.forename: Leonardo
authors.initials: L
authors.lastname: Rufiner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D006801:Humans; D008328:Man-Machine Systems; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface