An evolutionary computation approach to cognitive states classification
Rafael Ramírez, Montserrat Puiggròs · 2007
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.