Your Search Results

Use this resource - and many more! - in your textbook!

AcademicPub holds over eight million pieces of educational content for you to mix-and-match your way.

Experience the freedom of customizing your course pack with AcademicPub!
Not an educator but still interested in using this content? No problem! Visit our provider's page to contact the publisher and get permission directly.

Particle swarm optimization-based feature selection for cognitive state detection

By: Vogelstein, R.J.; Firpi, H.A.;

2011 / IEEE / 978-1-4577-1589-1

Description

This item was taken from the IEEE Conference ' 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.