title: Particle swarm optimization-based feature selection for cognitive state detection.
abstract: This manuscript proposes a particle swarm-based feature extraction to monitors brain activity with the goal of identifying correlate cognitive states and intensity of a task. This in turn would allow us to develop a pattern recognition system that will classify such cognitive states and thus to redistribute the workload to other subjects. In this abstract, we present a recognition system that employ multiple features from different domains, a feature selection method using a Particle Swarm Optimization (PSO) search algorithm while the classification is provided using a k-nearest neighbor. Through this approach, we are able to achieve an averaged classification accuracy of 90.25% on held-out, cross-validated data among the eight subjects.
authors.affiliation: Johns Hopkins University/Applied Physics Laboratory, Laurel, MD 20723-6009, USA. Alexer.Firpi@jhuapl.edu
authors.forename: H Alexer
authors.initials: HA
authors.lastname: Firpi
authors.forename: R Jacob
authors.initials: RJ
authors.lastname: Vogelstein
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D003071:Cognition; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D008328:Man-Machine Systems; D008889:Military Personnel; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014481:United States; D014584:User-Computer Interface