A Genetic Programming Approach to Feature Selection and Classification of Instantaneous Cognitive States

Authors:
Rafael Ramirez;Montserrat Puiggros
Affiliations:
Music Technology Group, Universitat Pompeu Fabra, Ocata 1, 08003 Barcelona, Spain;Music Technology Group, Universitat Pompeu Fabra, Ocata 1, 08003 Barcelona, Spain
Venue:
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Year:
2009

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Abstract

The study of human brain functions has dramatically increased in recent years greatly due to the advent of Functional Magnetic Resonance Imaging. This paper presents a genetic programming approach to the problem of classifying the instantaneous cognitive state of a person based on his/her functional Magnetic Resonance Imaging data. The problem provides a very interesting case study of training classifiers with extremely high dimensional, sparse and noisy data. We apply genetic programming for both feature selection and classifier training. We present a successful case study of induced classifiers which accurately discriminate between cognitive states produced by listening to different auditory stimuli.