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.
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Ramirez, R., Puiggros, M. (2007). A Genetic Programming Approach to Feature Selection and Classification of Instantaneous Cognitive States. In: Giacobini, M. (eds) Applications of Evolutionary Computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71805-5_34
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DOI: https://doi.org/10.1007/978-3-540-71805-5_34
Publisher Name: Springer, Berlin, Heidelberg
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