Abstract
The electroencephalography (EEG) is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. To gain insight about the spatial dynamics of the EEG, one has to solve the inverse problem, i.e., finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipole 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. 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 on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs <40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g., in epileptology and real-time applications.
Highlights
We evaluate the performance of ConvDip and compare it to state-of-the-art inverse algorithms, namely eLORETA, linear constrained minimum variance (LCMV) beamformer, and cMEM
We have presented ConvDip, a convolutional neural network (CNN) that aims to find extended sources of EEG signals
One major result of this work is that the CNN ConvDip was capable to reliably find the locations of neural sources that give rise to the EEG
Summary
Electroencephalography (EEG) is among the most used imaging techniques for noninvasive measurements of electromagnetic brain activity. The. ConvDip: EEG Source Imaging non-invasive estimations of neural generators, based on their projections to the scalp electrodes/sensors, constitutes an inverse problem. Invasive multimodal methods (e.g., using combined EEG and electrocorticography) help to bridge the gap between scalp recordings and neural generators, and in handling the inverse problem in this constellation. Access to these methods is limited and the conclusions that can be drawn are constrained by various factors such as placement of the cortical electrodes or coverage of brain areas that project to the scalp electrodes. The costs of this technique are considerably high and the relation between electromagnetic and metabolic dynamics is yet not fully understood
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