title: An automated method for relevant frequency bands identification based on genetic algorithms and dedicated to the Motor Imagery BCI protocol.
abstract: This paper presents an automated method for relevant frequency bands identification to be used in a left/right hand motor imagery based Brain Computer Interface system. The adopted optimization method aimed at maximizing the ratio between the mutual information and the error rate obtained using a Regularized Linear Discriminant Analysis (RLDA) based classifier and band-specific amplitude modulated envelopes as features. The search problem was handled by a genetic algorithm starting from an initial population determined on the basis of a-priori mu and beta relevant frequency bands identified by means of a standard power spectral density analysis between the idle and the left/right imagery data subset.
authors.affiliation: Bioengineering Department of Politecnico di Milano University, Milan 20133 Italy. sergio.parini@polimi.it
authors.forename: S
authors.initials: S
authors.lastname: Parini
authors.forename: L
authors.initials: L
authors.lastname: Maggi
authors.forename: G
authors.initials: G
authors.lastname: Andreoni
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D001921:Brain / Q000473:pathology*; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods; D004867:Equipment Design; D019054:Evoked Potentials, Motor; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D009068:Movement; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface