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Found 22 papers, 10 papers with code
A Conversational Brain-Artificial Intelligence Interface
We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs).
Efficient SAGE Estimation via Causal Structure Learning
We propose $d$-SAGE, a method that accelerates SAGE approximation.
Improvement-Focused Causal Recourse (ICR)
We demonstrate that given correct causal knowledge, ICR, in contrast to existing approaches, guides towards both acceptance and improvement.
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
Thus, an action that changes the prediction in the desired way may not lead to an improvement of the underlying target.
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
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.
A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
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.
A Distance Correlation-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
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.
Relative Feature Importance
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
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.
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware
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
Measurement Dependence Inducing Latent Causal Models
We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models.
Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail.
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
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.
A note on the expected minimum error probability in equientropic channels
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.
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
Causal inference concerns the identification of cause-effect relationships between variables.
Causal and anti-causal learning in pattern recognition for neuroimaging
Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models.
Decoding index finger position from EEG using random forests
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.
MERLiN: Mixture Effect Recovery in Linear Networks
Causal inference concerns the identification of cause-effect relationships between variables, e. g. establishing whether a stimulus affects activity in a certain brain region.
Causal interpretation rules for encoding and decoding models in neuroimaging
Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data.
Quantifying causal influences
Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy.
Statistics Theory Statistics Theory
Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing
EEG connectivity measures could provide a new type of feature space for inferring a subject's intention in Brain-Computer Interfaces (BCIs).
