Any kind of visual information is encoded in terms of patterns of neuralactivity occurring inside the brain. decodingneuralpatterns or its classification is a challenging task. Functional magnetic resonance imaging...
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ISBN:
(纸本)9781424492701
Any kind of visual information is encoded in terms of patterns of neuralactivity occurring inside the brain. decodingneuralpatterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activitypattern in term of images and electric potential respectively. To get higher spatiotemporal resolution of human brain from these two complementary neuroimaging modalities, simultaneous EEG-fMRI can be helpful. In this paper, we proposed a framework for classifying the brain activitypatterns with simultaneous EEG-fMRI. We have acquired five human participants' data with simultaneous EEG-fMRI by showing different object categories. Further, combined analysis of EEG and fMRI data was carried out. Extracted information through combine analysis is passed to support vector machine (SVM) classifier for classification purpose. We have achieved better classification accuracy using simultaneous EEG-fMRI i.e., 81.8% as compared to fMRI data standalone. This shows that multimodal neuroimaging can improve the classification accuracy of brain activitypatterns as compared to individual modalities reported in literature.
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