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Spatiotemporal graph convolutional neural networks for motor imagery EEG classification

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dc.contributor Graduate Program in Biomedical Engineering.
dc.contributor.advisor Ademoğlu, Ahmet. Alramly, Ahmed Mohamed Mohamed Mohamed. 2023-10-15T04:46:22Z 2023-10-15T04:46:22Z 2022
dc.identifier.other BM 2022 A68
dc.description.abstract Electroencephalography (EEG) has various applications in medicine, neuro-science, and neural engineering. It records the electrical activity of the brain tissues caused by the interaction among different neuronal communities. Numerous algorithms for the automatic classification of EEG signals have been developed. These algorithms work via extracting unique and non-redundant features from EEG signals. However, the majority of the proposed algorithms employ temporal components of EEG signals while disregarding the rich spatial network structure that underlies in the EEG. In this study, we propose a classification pipeline that uses the network structure of EEG data for a simultaneous representation of spatial and temporal features in the EEG signals. First, graph theory is utilized to model the EEG networks in two spatial domains; i) the sensor space and ii) the cortical source space. Second, a spatiotem- poral graph convolutional neural network (STGCNN) classification model is employed combining both temporal and spatial features of EEG data for its classification under motor imagery conditions. Additionally, the model is tested using the cortical source space data in each of the seven resting state brain networks individually to estimate their performance on classification accuracy. The results show that STGCNN model performs slightly better than the temporal convolutional neural network (CNN) models by 1.25%. NOTE Keywords : EEG, Spatiotemporal Graph Convolutional Neural Networks, Brain Networks.
dc.publisher Thesis (M.S.)-Bogazici University. Institute of Biomedical Engineering, 2022.
dc.subject.lcsh Electroencephalography.
dc.subject.lcsh Neurosciences.
dc.title Spatiotemporal graph convolutional neural networks for motor imagery EEG classification
dc.format.pages xiv, 62 leaves

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