Author

Ludger Tebartz van Elst

University of FreiburgORCID 0000-0002-2274-5447Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 4 Research Focus:benchmarking· 2 Approach:deep learning· 2 Organism:human· 2 Approach:statistical model· 2

Frequent Co-Authors

Jürgen KornmeieriD· 4 shared Lukas HeckeriD· 4 shared Rebekka Rupprecht· 2 shared Ad AertseniD· 1 shared Ellen JoosiD· 1 shared Mareike Wilson· 1 shared Moritz MaschkeiD· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 15 mutual co-author links

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4 shared papersNeuroscience

Jürgen KornmeierORCID

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4 papers

Recent·Most cited

Source Localization Using Recursively Applied and Projected MUSIC with Flexible Extent Estimation

Lukas Hecker, Ludger Tebartz van Elst, Jürgen Kornmeier

This paper introduces FLEX-MUSIC, a method for localizing neural sources in M/EEG data that also estimates source extent, achieving high accuracy and efficiency.

Frontiers in Neuroscience · May 15, 2023Peer-reviewedNeuroscience

Spontaneous Necker-Cube Reversals are not that Spontaneous

Mareike Wilson, Lukas Hecker, Ellen Joos, Ad Aertsen, Ludger Tebartz van Elst, Jürgen Kornmeier

The paper investigates EEG correlates of perceptual reversals in Necker cubes, finding early neural destabilization before spontaneous reversals, suggesting they are not as spontaneous as previously thought.

bioRxiv · Mar 6, 2023 · PreprintNeuroscience

Evaluation of Long-Short Term Memory Networks for M/EEG Source Imaging with Simulated and Real EEG Data

Lukas Hecker, Moritz Maschke, Rebekka Rupprecht, Ludger Tebartz van Elst, Jürgen Kornmeier

This paper evaluates LSTM networks for M/EEG source imaging using simulated and real EEG data, finding higher accuracy compared to classical methods and alternative architectures.

bioRxiv · Apr 13, 2022 · PreprintNeuroscience

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

Lukas Hecker, Rebekka Rupprecht, Ludger Tebartz van Elst, Jürgen Kornmeier

ConvDip is a convolutional neural network that solves the EEG inverse problem using a distributed dipole model, outperforming existing methods in accuracy and efficiency.

Frontiers in Neuroscience · Jun 9, 2021Peer-reviewedNeuroscience