119 research outputs found
Second-order networks in PyTorch
International audienceClassification of Symmetric Positive Definite (SPD) matrices is gaining momentum in a variety machine learning application fields. In this work we propose a Python library which implements neural networks on SPD matrices, based on the popular deep learning framework Pytorch
Channel Selection Procedure using Riemannian distance for BCI applications
International audienceThis article describes a new algorithm to select a subset of electrodes in BCI experiments. It is illustrated on a two-class motor imagery paradigm. The proposed approach is based on the Riemannian distance between spatial covariance matrices which allows to indirectly assess the discriminability between classes. Sensor selection is automatically done using a backward elimination principle. The method is tested on the dataset IVa from BCI competition III. The identified subsets are both consistent with neurophysiological principles and effective, achieving optimal performances with a reduced number of channels
MOABB: Trustworthy algorithm benchmarking for BCIs
BCI algorithm development has long been hampered by two major issues: small
sample sets and a lack of reproducibility. We offer a solution to both of these
problems via a software suite that streamlines both the issues of finding and
preprocessing data in a reliable manner, as well as that of using a consistent
interface for machine learning methods. By building on recent advances in
software for signal analysis implemented in the MNE toolkit, and the unified
framework for machine learning offered by the scikit-learn project, we offer a
system that can improve BCI algorithm development. This system is fully
open-source under the BSD licence and available at
https://github.com/NeuroTechX/moabb. To validate our efforts, we analyze a set
of state-of-the-art decoding algorithms across 12 open access datasets, with
over 250 subjects. Our analysis confirms that different datasets can result in
very different results for identical processing pipelines, highlighting the
need for trustworthy algorithm benchmarking in the field of BCIs, and further
that many previously validated methods do not hold up when applied across
different datasets, which has wide-reaching implications for practical BCIs
Spatial filtering optimisation in motor imagery EEG-based BCI
ISBN : 978-2-9532965-0-1Common spatial pattern (CSP) is becoming a standard way to combine linearly multi-channel EEG data in order to increase discrimination between two motor imagery tasks. We demonstrate in this article that the use of robust estimates allow improving the quality of CSP decomposition and CSP-based BCI. Furthermore, an evolutionary algorithm (EA)-type for electrode subset selection is proposed. It is shown that CSP with the obtained subset electrode yield comparable results with the ones obtained with CSP over large multi-channel recordings
Commande robuste d'un effecteur par une interface cerveau machine EEG asynchrone
This thesis presents the development of a Brain computer Interface (BCI) based on EEG signal, allowing its user to communicates with an external device solely by the mean of brain activity. This work as been conduct with the goal of designing a robust, ergonomic and easy to use BCI system for real life applications.In this context, a brain-switch has been developed, allowing it's user to send a binary command to a homeautomation system. This goal can only be achieved by developing new methodologies and algorithms, while testing them on real life experiments. Therefore, this works is two part, the first one is focus on the design of new algorithms, the secondon the design of experimental paradigm.Cette thèse a pour but le développement d’une Interface cerveau-machine (ICM) à partir de la mesure EEG,permettant à l’utilisateur de communiquer avec un dispositif externe directement par l’intermédiaire de son activité cérébrale. Ces travaux ont été menés avec comme ligne directrice le développement d'un système d'ICM utilisable dans un contexte de vie courante, le but étant de réaliser une ICM simple d'utilisation, robuste et ergonomique, permettant le contrôle d'un effecteur avec un temps de calibration minimal.Un brain-switch ou interrupteur cérébral a été réalisé et permet à l'utilisateur d'envoyer une commande binaire. La réalisation d'une telle ICM implique le développement d'algorithmes robustes et leurs mises en œuvre expérimentales. Les travaux réalisés comportent deux volets, l'un concerne le développement de nouveaux algorithmes, l'autre concerne la réalisation de campagne de tests
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
A Fixed-Point Algorithm for Estimating Power Means of Positive Definite Matrices
International audienceThe estimation of means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD) matrices is of great utility in classification problems and is currently heavily studied. The power means of SPD matrices with exponent p in the interval [-1, 1] interpolate in between the Harmonic (p =-1) and the Arithmetic mean (p = 1), while the Geometric (Karcher) mean corresponds to their limit evaluated at 0. In this article we present a simple fixed point algorithm for estimating means along this whole continuum. The convergence rate of the proposed algorithm for p = ±0.5 deteriorates very little with the number and dimension of points given as input. Along the whole continuum it is also robust with respect to the dispersion of the points on the manifold. Thus, the proposed algorithm allows the efficient estimation of the whole family of power means, including the geometric mean
Filtrage spatial robuste à partir d'un sous-ensemble optimal d'électrodes en BCI EEG
La réalisation d'une interface cerveau machine EEG nécessite généralement l'utilisation d'un grand nombre d'électrodes, causant la gêne de l'utilisateur et augmentant considérablement le coût calculatoire des traitements. Cependant, un choix judicieux de l'emplacement des ces électrodes peut permettre une réduction importante de leur nombre sans perte significative en performance. Cet article présente une méthode de sélection automatique d'un sous-ensemble quasi optimal d'électrodes et de filtres spatiaux calculés par Common Spatial Pattern (CSP) . Cette méthode, basée sur un calcul de coefficient de détermination multiple et l'utilisation du critère d'Akaike, est traitée de manière à résister aux artefacts par l'utilisation d'estimateurs robustes de variance et de matrice de covariance . Il est ainsi montré qu'une réduction très importante du nombre d'électrode est possible sans perte d'information sur les caractéristiques spatiales et que cette méthode résiste parfaitement à un grand nombre d'artefacts lorsque les signaux sont corrompus par des artefacts
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