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Found 18 papers, 11 papers with code
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
The significance of this study lies in its contribution to establishing a rigorous and transparent benchmark for BCI research, offering insights into optimal methodologies and highlighting the importance of reproducibility in driving advancements within the field.
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.
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability.
A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding
In this work, we systematically evaluate the effect of EA combined with DL for decoding BCI signals.
Evaluating the structure of cognitive tasks with transfer learning
While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, which is not the case in this setting.
End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities
In brain-computer interfaces (BCI), most of the approaches based on event-related potential (ERP) focus on the detection of P300, aiming for single trial classification for a speller task.
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.
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI).
Functional connectivity ensemble method to enhance BCI performance (FUCONE)
Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and that is mostly applied for brain activity analysis.
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020.
Online SSVEP-based BCI using Riemannian geometry
We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible.
From Euclidean to Riemannian Means: Information Geometry for SSVEP Classification
Brain Computer Interfaces (BCI) based on electroencephalog-raphy (EEG) rely on multichannel brain signal processing.
Adaptive Visualisation System for Construction Building Information Models Using Saliency
Building Information Modeling (BIM) is a recent construction process based on a 3D model, containing every component related to the building achievement.
Using Riemannian geometry for SSVEP-based Brain Computer Interface
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results.
On the need for metrics in dictionary learning assessment
Dictionary-based approaches are the focus of a growing attention in the signal processing community, often achieving state of the art results in several application fields.
Subspace metrics for multivariate dictionaries and application to EEG
Overcomplete representations and dictionary learning algorithms are attracting a growing interest in the machine learning community.
Metrics for Multivariate Dictionaries
Despite a recurrent need to rely on a distance for learning or assessing multivariate overcomplete representations, no metrics in their underlying spaces have yet been proposed.
SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system
How to enforce such a division in a decentralized and distributed way, is tackled in this paper, using a spiking neuron network architecture.
