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A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA

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. (2018) A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA. Australasian Physical and Engineering Sciences in Medicine . pp. 1-13.

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Abstract

Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7 for EEG data set and 64.1 for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level. © 2018 Australasian College of Physical Scientists and Engineers in Medicine

Item Type:Article
Impact Factor:cited By 0; Article in Press
Uncontrolled Keywords:Activation analysis; Brain; Decoding; Electroencephalography; Electrophysiology; Magnetic resonance imaging; Multivariant analysis; Neurophysiology; Support vector machines, Activation patterns; Brain activity patterns; Classification analysis; fMRI; Functional magnetic resonance imaging; Multivariate pattern analysis; Neuronal activities; Visual decoding, Functional neuroimaging
ID Code:20847
Deposited By: Ahmad Suhairi
Deposited On:26 Feb 2019 02:31
Last Modified:26 Feb 2019 02:31

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