Search Results for author:
Found 23 papers, 7 papers with code
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.
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.
Causal and anti-causal learning in pattern recognition for neuroimaging
Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models.
Deep learning with convolutional neural networks for EEG decoding and visualization
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
Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills
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.
Brain Responses During Robot-Error Observation
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.
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus.
Deep Transfer Learning for Error Decoding from Non-Invasive EEG
We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects).
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
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.
Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding
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.
Intracranial Error Detection via Deep Learning
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG).
Training Generative Reversible Networks
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
Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data.
A large-scale evaluation framework for EEG deep learning architectures
EEG is the most common signal source for noninvasive BCI applications.
Compact representations and pruning in residual networks
This criterion is based on the convergence of the neural dynamics in the last two successive layers of the residual block.
Deep Invertible Networks for EEG-based brain-signal decoding
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.
Machine-Learning-Based Diagnostics of EEG Pathology
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.
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
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.
Towards a Governance Framework for Brain Data
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.
When less is more: Simplifying inputs aids neural network understanding
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.
The spatial scale dimension of speech processing in the human brain
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.
Deep Riemannian Networks for EEG Decoding
Our study aims to lay the groundwork in the area of these topics through the analysis of DRNs for EEG with a wide range of hyperparameters.
Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learning
Furthermore, the brain age gap biomarker is not indicative of pathological EEG.
