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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

Zafar, R. and Kamel, N. and Naufal, M. and Malik, A.S. and Dass, S.C. and Ahmad, R.F. and Abdullah, J.M. and Reza, F. (2017) Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network. Journal of Integrative Neuroscience, 16 (3). pp. 275-289.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t -test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6) compared to ROI (61.88) and estimation values (64.17). © 2017 - IOS Press and the authors. All rights reserved.

Item Type:Article
Impact Factor:cited By 0
Departments / MOR / COE:Centre of Excellence > Center for Intelligent Signal and Imaging Research
ID Code:19834
Deposited By: Ahmad Suhairi
Deposited On:22 Apr 2018 13:06
Last Modified:22 Apr 2018 13:06

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