Apr 9, 2020

ConvDip: A convolutional neural network for better M/EEG Source Imaging

BioRxiv : the Preprint Server for Biology
Lukas HeckerJ. Kornmeier


EEG and MEG are well-established non-invasive methods in neuroscientific research and clinical diagnostics. Both methods provide a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the EEG one has to solve the inverse problem, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. A large number of approaches have been developed in the past to handle the inverse problem by creating more accurate and reliable solutions. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data in a semi-supervised approach. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods eLORETA and LCMV beamforming) on all focused performance measures. (3) It is more flexible when d...Continue Reading

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