Prediction of Human Brain Activity Using Likelihood Ratio Based Score Fusion

• Main Authors: Zafar, R., Dass, S.C., Malik, A.S., Kamel, N., Rehman, M.J.U., Ahmad, R.F., Abdullah, J.M., Reza, F.
• Format: Article
• Published: Institute of Electrical and Electronics Engineers Inc. 2017
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029297221&doi=10.1109%2fACCESS.2017.2698068&partnerID=40&md5=a54590a60a7a07308ffac50d172cd02b; http://eprints.utp.edu.my/19844/

Human brain has a complex structure with the billions of neurons, so it is a difficult and challenging task to predict the behavior of human brain. Different methods and classifiers are used to measure and classify the brain activities with higher accuracy and reliability. In this paper, instead of using mostly used classifier (support vector machine), prediction of the brain activity is done by estimating the match score densities. This method is based on likelihood ratio test which helps in finding the optimal combination of match scores. The distributions of match scores are modeled for different classes based on density score fusion in which the densities of different classes are estimated from the training data set and match scores are found by fusing the estimated densities with the testing data. The fusion is done with the data extracted from distributed activation patterns using multivariate pattern analysis (MVPA) against a visual task. MVPA is an intense strategy which helps in better understanding of the human brain. The match score-based technique is used in different biometric systems but never been used for the prediction of brain activity. In order to test the performance of proposed method, prediction accuracy is compared with the support vector machine using two data sets of different modalities, one is electroencephalography (EEG) and the other is functional magnetic resonance imaging (fMRI). The results show that the proposed method predicts the novel data with improved accuracy of 66.1 and 69.3 compared with support vector machine which have 64.15 and 65.7 for fMRI and EEG data sets, respectively. © 2017 IEEE.