110 research outputs found
Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry
International audienceThe classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair
A New Generation of Brain-Computer Interface Based on Riemannian Geometry
Based on the cumulated experience over the past 25 years in the field of
Brain-Computer Interface (BCI) we can now envision a new generation of BCI.
Such BCIs will not require training; instead they will be smartly initialized
using remote massive databases and will adapt to the user fast and effectively
in the first minute of use. They will be reliable, robust and will maintain
good performances within and across sessions. A general classification
framework based on recent advances in Riemannian geometry and possessing these
characteristics is presented. It applies equally well to BCI based on
event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state
evoked potential (SSEP). The framework is very simple, both algorithmically and
computationally. Due to its simplicity, its ability to learn rapidly (with
little training data) and its good across-subject and across-session
generalization, this strategy a very good candidate for building a new
generation of BCIs, thus we hereby propose it as a benchmark method for the
field.Comment: 33 pages, 9 Figures, 17 equations/algorithm
The Riemannian Minimum Distance to Means Field Classifier
International audienc
Robust Brain-computer interface for virtual Keyboard (RoBIK): project results
Special issue : ANR TECSAN : Technologies for Health and AutonomyNational audienceBrain-ComputerInterface (BCI)is a technology that translatesthe brain electrical activity into a command for a device such as a robotic arm, a wheelchair or a spelling device. BCIs have long been described as an assistive technology forseverely disabled patients because they completely bypass the need for muscular activity. The clinical reality is however dramatically different and most patients who use BCIs today are doing so as part of constraining clinical trials. To achieve the technological transfer from bench to bedside, BCI must gain ease of use and robustness of bothmeasure (electroencephalography [EEG]) and interface (signal processing and applications).TheRobustBrain-computerInterface for virtual Keyboard (RoBIK) project aimed atthe development of aBCIsystemfor communication that could be used on a daily basis by patientswithoutthe help of a trained teamofresearchers.To guide further developments cliniciansfirst assessed patients' needs.The prototype subsequently developed consisted in a 14 felt-pad electrodes EEG headsetsampling at 256Hz by an electronic component capable of transmitting signals wirelessly. The application was a virtual keyboard generating a novelstimulation paradigm to elicit P300 Evoked Related Potentials(ERPs) for communication. Raw EEG signals were treated with OpenViBE open-source software including novelsignal processing and stimulation techniques
BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces
There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI).
Publicly available datasets are usually limited by small number of participants with few
BCI sessions. In this sense, the lack of large, comprehensive datasets with various
individuals and multiple sessions has limited advances in the development of more
effective data processing and analysis methods for BCI systems. This is particularly
evident to explore the feasibility of deep learning methods that require large datasets.
Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder
individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a
total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge
organized during MEDICON 2019 where, in two phases, teams from all over the world
tried to achieve the best possible object-detection accuracy based on the P300 signals.
This paper presents the characteristics of the dataset and the approaches followed by
the 9 finalist teams during the competition. The winner obtained an average accuracy
of 92.3% with a convolutional neural network based on EEGNet. The dataset is now
publicly released and stands as a benchmark for future P300-based BCI algorithms
based on multiple session data
"Brain Invaders 2" : an open source Plug & Play multi-user BCI videogame
International audienceIntroduction: In 2011 we proposed « Brain Invaders » [1], a BCI videogame inspired from the vintage game Space Invaders. The software was released open source and was compatible with OpenVIBE [2]. The system is based on ERP classification using the oddball paradigm with a grid of 36 possible targets. This second version extends the game to the multiuser scenario. It includes four game modes namely Solo, Collaboration, Cooperation, Competition which are suitable for hyperscanning studies. Thanks to a classification algorithm based on Riemannian geometry, the system shows very good accuracy and is fully " Plug & Play " , no calibration phase is needed
Recreational Applications of OpenViBE: Brain Invaders and Use-the-Force
International audienceThis chapter aims at providing the reader with two examples of open-source BCI-games that work with the OpenViBE platform. These two games are “Brain Invaders” and “Use-The-Force!” and are representative examples of two types of BCI: ERP-based BCI and oscillatory activity-based BCI. This chapter presents the principle, design and evaluation of these games, as well as how they are implemented in practice within OpenViBE. This aims at providing the interested readers with a practical basis to design their own BCI-based games
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Riemannian geometry has been successfully used in many brain-computer
interface (BCI) classification problems and demonstrated superior performance.
In this paper, for the first time, it is applied to BCI regression problems, an
important category of BCI applications. More specifically, we propose a new
feature extraction approach for Electroencephalogram (EEG) based BCI regression
problems: a spatial filter is first used to increase the signal quality of the
EEG trials and also to reduce the dimensionality of the covariance matrices,
and then Riemannian tangent space features are extracted. We validate the
performance of the proposed approach in reaction time estimation from EEG
signals measured in a large-scale sustained-attention psychomotor vigilance
task, and show that compared with the traditional powerband features, the
tangent space features can reduce the root mean square estimation error by
4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291
Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces
We propose a fusion approach that combines features from simultaneously
recorded electroencephalographic (EEG) and magnetoencephalographic (MEG)
signals to improve classification performances in motor imagery-based
brain-computer interfaces (BCIs). We applied our approach to a group of 15
healthy subjects and found a significant classification performance enhancement
as compared to standard single-modality approaches in the alpha and beta bands.
Taken together, our findings demonstrate the advantage of considering
multimodal approaches as complementary tools for improving the impact of
non-invasive BCIs
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