ArticleFrontiers in neuroscience2021
ConvDip: A Convolutional Neural Network for Better EEG Source Imaging.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Structural Eigenmodes of the Brain to Improve the Source Localization of EEG: Application to Epileptiform Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Fully Automated EEG Source Imaging Using Structured Sparsity for Single and Multiple Synchronous Epileptic Activities.Brain topography · 2026Article
- Inferring neural sources from electroencephalography: foundations and frontiers.Journal of neural engineering · 2026Review
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Deep learning-based EEG source imaging is robust under varying electrode configurations.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Magnetoencephalographic source localization and reconstruction via deep learning.Frontiers in neuroscience · 2025Article
- Inferring laminar origins of MEG signals with optically pumped magnetometers (OPMs): A simulation study.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Structured noise champagne: an empirical Bayesian algorithm for electromagnetic brain imaging with structured noise.Frontiers in human neuroscience · 2025Article
- M/EEG source localization for both subcortical and cortical sources using a convolutional neural network with a realistic head conductivity model.APL bioengineering · 2024Article
- Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG.NeuroImage · 2024Article
- Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation.Brain informatics · 2024Article
- Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond.Frontiers in human neuroscience · 2024Review
- Comprehensive analysis of supervised learning methods for electrical source imaging.Frontiers in neuroscience · 2024Article
- Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.NeuroImage · 2023Article
- Source localization using recursively applied and projected MUSIC with flexible extent estimation.Frontiers in neuroscience · 2023Article
- Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Altered EEG variability on different time scales in participants with autism spectrum disorder: an exploratory study.Scientific reports · 2022Article
- A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging.Frontiers in neuroscience · 2022Article
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4 authors.
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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.
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