Search Results for author: Tonio Ball

Found 21 papers, 6 papers with code

The spatial scale dimension of speech processing in the human brain

no code implementations19 Jan 2022 Philipp Kellmeyer, Roland Berkemeier, Tonio Ball

As most fMRI studies only use a single filter for analysis, much information on the size and shape of the BOLD signal in Gaussian scale space remains hidden and constrains the interpretation of fMRI studies.

When less is more: Simplifying inputs aids neural network understanding

no code implementations14 Jan 2022 Robin Tibor Schirrmeister, Rosanne Liu, Sara Hooker, Tonio Ball

To answer these questions, we need a clear measure of input simplicity (or inversely, complexity), an optimization objective that correlates with simplification, and a framework to incorporate such objective into training and inference.

Dataset Condensation

Towards a Governance Framework for Brain Data

no code implementations24 Sep 2021 Marcello Ienca, Joseph J. Fins, Ralf J. Jox, Fabrice Jotterand, Silja Voeneky, Roberto Andorno, Tonio Ball, Claude Castelluccia, Ricardo Chavarriaga, Hervé Chneiweiss, Agata Ferretti, Orsolya Friedrich, Samia Hurst, Grischa Merkel, Fruzsina Molnar-Gabor, Jean-Marc Rickli, James Scheibner, Effy Vayena, Rafael Yuste, Philipp Kellmeyer

The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance.

Ethics

Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features

1 code implementation NeurIPS 2020 Robin Tibor Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang

We refine previous investigations of this failure at anomaly detection for invertible generative networks and provide a clear explanation of it as a combination of model bias and domain prior: Convolutional networks learn similar low-level feature distributions when trained on any natural image dataset and these low-level features dominate the likelihood.

Anomaly Detection

Machine-Learning-Based Diagnostics of EEG Pathology

1 code implementation11 Feb 2020 Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabąszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball

The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks.

EEG Machine Learning

Deep Invertible Networks for EEG-based brain-signal decoding

no code implementations17 Jul 2019 Robin Tibor Schirrmeister, Tonio Ball

In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals above chance.

EEG

Compact representations and pruning in residual networks

no code implementations20 Oct 2018 Fereshteh Lagzi, Tonio Ball, Joschka Boedecker

This criterion is based on the convergence of the neural dynamics in the last two successive layers of the residual block.

Training Generative Reversible Networks

1 code implementation5 Jun 2018 Robin Tibor Schirrmeister, Patryk Chrabąszcz, Frank Hutter, Tonio Ball

This first attempt to use RevNets inside the adversarial autoencoder framework slightly underperformed relative to recent advanced generative models using an autoencoder component on CelebA, but this gap may diminish with further optimization of the training setup of generative RevNets.

EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals

2 code implementations5 Jun 2018 Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball

Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data.

Data Augmentation EEG +1

Intracranial Error Detection via Deep Learning

no code implementations4 May 2018 Martin Völker, Jiří Hammer, Robin T. Schirrmeister, Joos Behncke, Lukas D. J. Fiederer, Andreas Schulze-Bonhage, Petr Marusič, Wolfram Burgard, Tonio Ball

Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG).

EEG

Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding

no code implementations21 Nov 2017 Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball

Our findings thus provide insights into how ConvNets hierarchically represent spectral EEG features in their intermediate layers and suggest that ConvNets can exploit and might help to better understand the compositional structure of EEG time series.

EEG Eeg Decoding +1

The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks

no code implementations16 Nov 2017 Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard, Tonio Ball

Analysis of brain signals from a human interacting with a robot may help identifying robot errors, but accuracies of such analyses have still substantial space for improvement.

EEG Eeg Decoding +1

Deep Transfer Learning for Error Decoding from Non-Invasive EEG

no code implementations25 Oct 2017 Martin Völker, Robin T. Schirrmeister, Lukas D. J. Fiederer, Wolfram Burgard, Tonio Ball

We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects).

EEG Transfer Learning

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

2 code implementations26 Aug 2017 Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger, Frank Hutter, Tonio Ball

We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus.

EEG

Brain Responses During Robot-Error Observation

no code implementations4 Aug 2017 Dominik Welke, Joos Behncke, Marina Hader, Robin Tibor Schirrmeister, Andreas Schönau, Boris Eßmann, Oliver Müller, Wolfram Burgard, Tonio Ball

Our findings suggest that non-invasive recordings of brain responses elicited when observing robots indeed contain decodable information about the correctness of the robot's action and the type of observed robot.

EEG

Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills

no code implementations20 Jul 2017 Felix Burget, Lukas Dominique Josef Fiederer, Daniel Kuhner, Martin Völker, Johannes Aldinger, Robin Tibor Schirrmeister, Chau Do, Joschka Boedecker, Bernhard Nebel, Tonio Ball, Wolfram Burgard

As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time.

Deep learning with convolutional neural networks for EEG decoding and visualization

5 code implementations15 Mar 2017 Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball

PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary. wiley. com/doi/10. 1002/hbm. 23730/full Code available here: https://github. com/robintibor/braindecode

EEG Eeg Decoding

Causal and anti-causal learning in pattern recognition for neuroimaging

no code implementations15 Dec 2015 Sebastian Weichwald, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup

Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models.

Causal Inference

Decoding index finger position from EEG using random forests

no code implementations14 Dec 2015 Sebastian Weichwald, Timm Meyer, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup

While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals.

EEG

Causal interpretation rules for encoding and decoding models in neuroimaging

no code implementations15 Nov 2015 Sebastian Weichwald, Timm Meyer, Ozan Özdenizci, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup

Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data.

EEG

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