Estimation of entropy and mutual information
Neural Computation
Detection of cognitive states from fMRI data using machine learning techniques
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Competitive repetition-suppression (core) learning
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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In the last few years, functional Magnetic Resonance Imaging (fMRI) has emerged as a new and powerful method to map the cognitive states of a human subject to specific functional areas of the brain. Although fMRI has been widely used to determine average activation in different brain regions, the problem of automatically decoding the cognitive state from instantaneous brain activations has only recently been investigated. We argue that machine learning might be an effective approach to deal with fMRI image interpretation when data are collected through a free design stimulation protocol. The brain decoding task can be shaped as a classification problem. Given in input the fMRI signal of the brain, a trained model can predict the corresponding cognitive state. This study investigates the use of recurrent neural network to interpret fMRI brain images. We present the results of an empirical analysis on PBAIC data. PBAIC, namely Pittsburgh Brain Activity Interpretation Contest, proposes a brain decoding problem based on free design protocol of stimuli.