1 code implementation • 22 Feb 2024 • Anja Meunier, Michal Robert Žák, Lucas Munz, Sofiya Garkot, Manuel Eder, Jiachen Xu, Moritz Grosse-Wentrup
We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs).
1 code implementation • 6 Apr 2023 • Christoph Luther, Gunnar König, Moritz Grosse-Wentrup
We propose $d$-SAGE, a method that accelerates SAGE approximation.
1 code implementation • 27 Oct 2022 • Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
We demonstrate that given correct causal knowledge, ICR, in contrast to existing approaches, guides towards both acceptance and improvement.
1 code implementation • 14 Feb 2022 • Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup, Alexandre Gramfort, Sylvain Chevallier, Vinay Jayaram, Camille Jeunet, Stylianos Bakas, Siegfried Ludwig, Konstantinos Barmpas, Mehdi Bahri, Yannis Panagakis, Nikolaos Laskaris, Dimitrios A. Adamos, Stefanos Zafeiriou, William C. Duong, Stephen M. Gordon, Vernon J. Lawhern, Maciej Śliwowski, Vincent Rouanne, Piotr Tempczyk
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
no code implementations • 16 Jul 2021 • Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
Thus, an action that changes the prediction in the desired way may not lead to an improvement of the underlying target.
1 code implementation • 15 Jun 2021 • Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup
Direct importance provides causal insight into the model's mechanism, yet it fails to expose the leakage of information from associated but not directly used variables.
no code implementations • 7 Jun 2021 • Alex Markham, Richeek Das, Moritz Grosse-Wentrup
Even stronger, we prove that the kernel space is isometric to the space of causal ancestral graphs, so that distance between samples in the kernel space is guaranteed to correspond to distance between their generating causal structures.
no code implementations • NeurIPS 2021 • Alex Markham, Moritz Grosse-Wentrup
We consider the problem of causal structure learning in the setting of heterogeneous populations, i. e., populations in which a single causal structure does not adequately represent all population members, as is common in biological and social sciences.
3 code implementations • 16 Jul 2020 • Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model.
1 code implementation • 8 Jul 2020 • Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly.
1 code implementation • 26 Feb 2020 • Matthias R. Hohmann, Lisa Konieczny, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup, Bernhard Schölkopf
We introduce MYND: A framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies.
Human-Computer Interaction Neurons and Cognition 68U35 H.5.2
no code implementations • 19 Oct 2019 • Alex Markham, Moritz Grosse-Wentrup
We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models.
no code implementations • 4 Jul 2017 • Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf
Complex systems can be modelled at various levels of detail.
no code implementations • 6 Jan 2017 • Leila Wehbe, Anwar Nunez-Elizalde, Marcel van Gerven, Irina Rish, Brian Murphy, Moritz Grosse-Wentrup, Georg Langs, Guillermo Cecchi
The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus.
no code implementations • 23 May 2016 • Sebastian Weichwald, Tatiana Fomina, Bernhard Schölkopf, Moritz Grosse-Wentrup
While the channel capacity reflects a theoretical upper bound on the achievable information transmission rate in the limit of infinitely many bits, it does not characterise the information transfer of a given encoding routine with finitely many bits.
1 code implementation • 2 May 2016 • Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup
Causal inference concerns the identification of cause-effect relationships between variables.
no code implementations • 15 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.
no code implementations • 14 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.
1 code implementation • 3 Dec 2015 • Sebastian Weichwald, Moritz Grosse-Wentrup, Arthur Gretton
Causal inference concerns the identification of cause-effect relationships between variables, e. g. establishing whether a stimulus affects activity in a certain brain region.
no code implementations • 15 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.
no code implementations • 29 Mar 2012 • Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf
Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy.
Statistics Theory Statistics Theory
no code implementations • NeurIPS 2008 • Moritz Grosse-Wentrup
EEG connectivity measures could provide a new type of feature space for inferring a subject's intention in Brain-Computer Interfaces (BCIs).