Particle swarm optimization-based feature selection for cognitive state detection

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.