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...
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doaj-40128401d27140a7aefb731a32bc50142020-11-25T01:40:12ZporUniversidade Nove de JulhoExacta1678-54281983-93082007-01-0152311320Discriminating brain activated area and predicting the stimuli performed using artificial neural networkRafael do Espírito SantoJoão Ricardo SatoMaria G. Moraes MartinIn 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.http://www.redalyc.org/articulo.oa?id=81050213 |
collection |
DOAJ |
language |
Portuguese |
format |
Article |
sources |
DOAJ |
author |
Rafael do Espírito Santo João Ricardo Sato Maria G. Moraes Martin |
spellingShingle |
Rafael do Espírito Santo João Ricardo Sato Maria G. Moraes Martin Discriminating brain activated area and predicting the stimuli performed using artificial neural network Exacta |
author_facet |
Rafael do Espírito Santo João Ricardo Sato Maria G. Moraes Martin |
author_sort |
Rafael do Espírito Santo |
title |
Discriminating brain activated
area and predicting the stimuli performed
using artificial neural network |
title_short |
Discriminating brain activated
area and predicting the stimuli performed
using artificial neural network |
title_full |
Discriminating brain activated
area and predicting the stimuli performed
using artificial neural network |
title_fullStr |
Discriminating brain activated
area and predicting the stimuli performed
using artificial neural network |
title_full_unstemmed |
Discriminating brain activated
area and predicting the stimuli performed
using artificial neural network |
title_sort |
discriminating brain activated
area and predicting the stimuli performed
using artificial neural network |
publisher |
Universidade Nove de Julho |
series |
Exacta |
issn |
1678-5428 1983-9308 |
publishDate |
2007-01-01 |
description |
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. |
url |
http://www.redalyc.org/articulo.oa?id=81050213 |
work_keys_str_mv |
AT rafaeldoespiritosanto discriminatingbrainactivatedareaandpredictingthestimuliperformedusingartificialneuralnetwork AT joaoricardosato discriminatingbrainactivatedareaandpredictingthestimuliperformedusingartificialneuralnetwork AT mariagmoraesmartin discriminatingbrainactivatedareaandpredictingthestimuliperformedusingartificialneuralnetwork |
_version_ |
1725046459211972608 |