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

  • Raheel Zafar
  • Nidal Kamel
  • Mohamad Naufal
  • Aamir Saeed Malik
  • Sarat C. Dass
  • Rana Fayyaz Ahmad
  • Jafri M. Abdullah
  • Faruque Reza
Scientific Paper
  • 174 Downloads

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.

Keywords

EEG fMRI Visual decoding SVM DWT 

Notes

Acknowledgements

This research is supported by the Ministry of Education (MOE), Malaysia under the Grant of Higher Institution Centre of Excellence (HICoE) for CISIR (Ref: 0153CA-002).

Compliance with ethical standards

Conflict of interest

Aamir Saeed Malik has received the above grant and he declares that he has no conflict of interest.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the Hospital Universiti Sains Malaysia (HUSM) and is already used in a recent study [34]. The study protocol was approved by the local ethics committee of HUSM.

Informed consent

Informed consent was obtained from all individual participants included in the study before the start of the experiment.

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  1. 1.Department of EngineeringNational University of Modern LanguagesIslamabadPakistan
  2. 2.Centre for Intelligent Signal and Imaging Research (CISIR)Universiti Teknologi PETRONASPerakMalaysia
  3. 3.Center for Neuroscience Services and ResearchUniversiti Sains MalaysiaKota BharuMalaysia
  4. 4.Department of Neurosciences, School of Medical SciencesUniversiti Sains MalaysiaKota BharuMalaysia

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