Tonio Ball | DeepAI

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Bernhard Schölkopf
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Sara Hooker
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Ozan Özdenizci
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Moritz Grosse-Wentrup
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Robin Tibor Schirrmeister
17 publications
Sebastian Weichwald
17 publications

∙ 12/20/2022

Deep Riemannian Networks for EEG Decoding

State-of-the-art performance in electroencephalography (EEG) decoding ta...

0 Daniel Wilson, et al. ∙

∙ 09/19/2022

A Framework for Preserving Privacy and Cybersecurity in Brain-Computer Interfacing Applications

Brain-Computer Interfaces (BCIs) comprise a rapidly evolving field of te...

0 Maryna Kapitonova, et al. ∙

∙ 01/14/2022

When less is more: Simplifying inputs aids neural network understanding

How do neural network image classifiers respond to simpler and simpler i...

42 Robin Tibor Schirrmeister, et al. ∙

∙ 09/24/2021

Towards a Governance Framework for Brain Data

The increasing availability of brain data within and outside the biomedi...

0 Marcello Ienca, et al. ∙

∙ 06/18/2020

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

Deep generative networks trained via maximum likelihood on a natural ima...

0 Robin Tibor Schirrmeister, et al. ∙

∙ 02/11/2020

Machine-Learning-Based Diagnostics of EEG Pathology

Machine learning (ML) methods have the potential to automate clinical EE...

13 Lukas Alexander Wilhelm Gemein, et al. ∙

∙ 08/14/2019

A Research Framework for Virtual Reality Neurosurgery Based on Open-Source Tools

Fully immersive virtual reality (VR) has the potential to improve neuros...

0 Lukas D. J. Fiederer, et al. ∙

∙ 07/17/2019

Deep Invertible Networks for EEG-based brain-signal decoding

In this manuscript, we investigate deep invertible networks for EEG-base...

0 Robin Tibor Schirrmeister, et al. ∙

∙ 07/04/2018

The role of robot design in decoding error-related information from EEG signals of a human observer

For utilization of robotic assistive devices in everyday life, means for...

0 Joos Behncke, et al. ∙

∙ 06/20/2018

Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding

When it comes to the classification of brain signals in real-life applic...

0 Joos Behncke, et al. ∙

∙ 06/18/2018

A large-scale evaluation framework for EEG deep learning architectures

EEG is the most common signal source for noninvasive BCI applications. F...

0 Felix A. Heilmeyer, et al. ∙

∙ 06/18/2018

A framework for large-scale evaluation of deep learning for EEG

EEG is the most common signal source for noninvasive BCI applications. F...

0 Felix A. Heilmeyer, et al. ∙

∙ 06/05/2018

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

Generative adversarial networks (GANs) are recently highly successful in...

0 Kay Gregor Hartmann, et al. ∙

∙ 06/05/2018

Generative Reversible Networks

Generative models with an encoding component such as autoencoders curren...

2 Robin Tibor Schirrmeister, et al. ∙

∙ 06/05/2018

Training Generative Reversible Networks

Generative models with an encoding component such as autoencoders curren...

0 Robin Tibor Schirrmeister, et al. ∙

∙ 05/04/2018

Intracranial Error Detection via Deep Learning

Deep learning techniques have revolutionized the field of machine learni...

2 Martin Völker, et al. ∙

∙ 11/21/2017

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

Recently, there is increasing interest and research on the interpretabil...

0 Kay Gregor Hartmann, et al. ∙

∙ 11/16/2017

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

The importance of robotic assistive devices grows in our work and everyd...

0 Joos Behncke, et al. ∙

∙ 10/25/2017

Deep Transfer Learning for Error Decoding from Non-Invasive EEG

We recorded high-density EEG in a flanker task experiment (31 subjects) ...

0 Martin Völker, et al. ∙

∙ 08/26/2017

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

We apply convolutional neural networks (ConvNets) to the task of disting...

0 Robin Tibor Schirrmeister, et al. ∙

∙ 08/04/2017

Brain Responses During Robot-Error Observation

Brain-controlled robots are a promising new type of assistive device for...

0 Dominik Welke, et al. ∙

∙ 07/20/2017

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

As autonomous service robots become more affordable and thus available a...

0 Felix Burget, et al. ∙

∙ 03/15/2017

Deep learning with convolutional neural networks for EEG decoding and visualization

A revised version of this article is now available at Human Brain Mappin...

0 Robin Tibor Schirrmeister, et al. ∙

∙ 12/15/2015

Causal and anti-causal learning in pattern recognition for neuroimaging

Pattern recognition in neuroimaging distinguishes between two types of m...

0 Sebastian Weichwald, et al. ∙

∙ 12/14/2015

Decoding index finger position from EEG using random forests

While invasively recorded brain activity is known to provide detailed in...

0 Sebastian Weichwald, et al. ∙

∙ 11/15/2015

Causal interpretation rules for encoding and decoding models in neuroimaging

Causal terminology is often introduced in the interpretation of encoding...

0 Sebastian Weichwald, et al. ∙

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