Affiliations: [a] Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia | [b] Department of Neuroscience, Universiti Sains Malaysia, Kota Bharu, Kelantan, Malaysia | [c] Department of Computer Science, King Saud University, Riyadh, Saudi Arabia
Correspondence:
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Corresponding author: Aamir Saeed Malik, Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia. E-mail:[email protected]
Abstract: BACKGROUND: Classification of the visual information from the brain
activity data is a challenging task. Many studies reported in the literature
are based on the brain activity patterns using either fMRI or EEG/MEG only.
EEG and fMRI considered as two complementary neuroimaging modalities in
terms of their temporal and spatial resolution to map the brain activity.
For getting a high spatial and temporal resolution of the brain at the same
time, simultaneous EEG-fMRI seems to be fruitful. METHODS: In this article, we propose a new method based on
simultaneous EEG-fMRI data and machine learning approach to classify the
visual brain activity patterns. We acquired EEG-fMRI data simultaneously on
the ten healthy human participants by showing them visual stimuli. Data
fusion approach is used to merge EEG and fMRI data. Machine learning
classifier is used for the classification purposes. RESULTS: Results showed that superior classification performance
has been achieved with simultaneous EEG-fMRI data as compared to the EEG and
fMRI data standalone. This shows that multimodal approach improved the
classification accuracy results as compared with other approaches reported
in the literature. CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for
classifying the brain activity patterns can be helpful to predict or fully
decode the brain activity patterns.
Keywords: EEG, fMRI, simultaneous EEG-fMRI, data fusion, classification
