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Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data
2018
Journal of Engineering
We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which
doi:10.1155/2018/1350692
fatcat:tj4ubmzqxrbvdkaa5mtgoj4jue