Discriminating brain activated area and predicting the stimuli performed using artificial neural network

Abstract

In this work, a Multilayer Perceptron implementation – MLP using functional Magnetic Resonance Imaging (fMRI) is used to infer stimuli performed. Sets of images of brain activation were generated by visual, auditory and finger tapping paradigms in 54 healthy volunteers. These images were used for training the MLP network in a leave-one-out manner in order to predict the paradigm that a subject performed by using other images, so far unseen by the MLP network. The aim in this paper is the exploring of the influence of the number of the Principal Component (PC) on the performance of the MLP in classifying fMRI paradigms. The classifier’s performance was evaluated in terms of the Sensitivity and Specificity, Prediction Accuracy and the area Az under the receiver operating characteristics (ROC) curve. From the ROC analysis, values of Az up to 1 were obtained with 60 PCs in discriminating the visual paradigm from the auditory paradigm.