Search Results for author:
Found 7 papers, 2 papers with code
Geometric Neural Network based on Phase Space for BCI decoding
\textbf{Approach:} Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes.
Pseudo-online framework for BCI evaluation: A MOABB perspective
The main difference is that the offline mode often analyzes the whole data, while the online and pseudo-online modes only analyze data in short time windows.
An embedding for EEG signals learned using a triplet loss
In offline analyses using EEG data of 14 subjects, we tested the embeddings' feasibility and compared their efficiency with state-of-the-art deep learning models and conventional machine learning pipelines.
Classification of BCI-EEG based on augmented covariance matrix
A fairly natural idea is therefore to extend the standard approach using these augmented covariance matrices.
Embedding neurophysiological signals
Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e. g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e. g., in a demanding work environment.
Data-driven cortical clustering to provide a family of plausible solutions to M/EEG inverse problem
The M/EEG inverse problem is ill-posed.
Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals
This is the case, for instance, in multi-trial magneto- or electroencephalography (M/EEG).
