Discriminating brain activated area and predicting the stimuli performed using artificial neural network
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Portuguese |
| Published: |
Universidade Nove de Julho
2007-01-01
|
| Series: | Exacta |
| Online Access: | http://www.redalyc.org/articulo.oa?id=81050213 |
| Summary: | 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. |
|---|---|
| ISSN: | 1678-5428 1983-9308 |