TY  - JOUR
Y1  - 2018///
VL  - 41
A1  - Zafar, R.
A1  - Kamel, N.
A1  - Naufal, M.
A1  - Malik, A.S.
A1  - Dass, S.C.
A1  - Ahmad, R.F.
A1  - Abdullah, J.M.
A1  - Reza, F.
JF  - Australasian Physical and Engineering Sciences in Medicine
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048466910&doi=10.1007%2fs13246-018-0656-5&partnerID=40&md5=15cf391d75bc0278fce006b358a38b86
ID  - scholars10055
KW  - Activation analysis; Brain; Decoding; Electroencephalography; Electrophysiology; Magnetic resonance imaging; Multivariant analysis; Neurophysiology; Support vector machines
KW  -  Activation patterns; Brain activity patterns; Classification analysis; fMRI; Functional magnetic resonance imaging; Multivariate pattern analysis; Neuronal activities; Visual decoding
KW  -  Functional neuroimaging
KW  -  Article; brain analysis; brain depth stimulation; brain region; controlled study; data analysis; data processing; electroencephalogram; functional magnetic resonance imaging; human; learning algorithm; mathematical computing; neuroimaging; nonhuman; spatiotemporal analysis; support vector machine; task performance; adult; algorithm; behavior; brain; brain mapping; electroencephalography; female; image processing; male; multivariate analysis; nuclear magnetic resonance imaging; physiology; young adult
KW  -  Adult; Algorithms; Behavior; Brain; Brain Mapping; Electroencephalography; Female; Humans; Image Processing
KW  -  Computer-Assisted; Magnetic Resonance Imaging; Male; Multivariate Analysis; Young Adult
N2  - 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.
IS  - 3
PB  - Springer Netherlands
SN  - 01589938
EP  - 645
AV  - none
TI  - A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA
SP  - 633
N1  - cited By 3
ER  -