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Objective We tested the hypothesis that somatosensory system injury would more strongly affect movement than motor system injury in children with unilateral cerebral palsy (USCP). This hypothesis was based on how somatosensory and corticospinal circuits adapt to injury during development; whereas the motor system can maintain connections to the impaired hand from the uninjured hemisphere, this does not occur in the somatosensory system. As a corollary, cortical injury strongly impairs sensory function, so we hypothesized that cortical lesions would impair hand function more than subcortical lesions. Methods Twenty-four children with unilateral cerebral palsy had physiological and anatomical measures of the motor and somatosensory systems and lesion classification. Motor physiology was performed with transcranial magnetic stimulation and somatosensory physiology with vibration-evoked electroencephalographic potentials. Tractography of the corticospinal tract and the medial lemniscus was performed with diffusion tensor imaging, and lesions were classified by magnetic resonance imaging. Anatomical and physiological results were correlated with measures of hand function using 2 independent statistical methods. Results Children with disruptions in the somatosensory connectivity and cortical lesions had the most severe upper extremity impairments, particularly somatosensory function. Motor system connectivity was significantly correlated with bimanual function, but not unimanual function or somatosensory function. Interpretation Both sensory and motor connectivity impact hand function in children with USCP. Somatosensory connectivity could be an important target for recovery of hand function in children with USCP. Ann Neurol 2017;82:766–780

The objective of this study was to assess whether the novel application of a machine learning approach to data collected from the Microsoft Kinect 2 (MK2) could be used to classify differing levels of upper limb impairment. Twenty-four healthy subjects completed items of the Wolf Motor Function Test (WMFT), which is a clinically validated metric of upper limb function for stroke survivors. Subjects completed the WMFT three times: 1) as a healthy individual; 2) emulating mild impairment; and 3) emulating moderate impairment. A MK2 was positioned in front of participants, and collected kinematic data as they completed the WMFT. A classification framework, based on Riemannian geometry and the use of covariance matrices as feature representation of the MK2 data, was developed for these data, and its ability to successfully classify subjects as either "healthy," "mildly impaired," or "moderately impaired" was assessed. Mean accuracy for our classifier was 91.7%, with a specific accuracy breakdown of 100%, 83.3%, and 91.7% for the "healthy," "mildly impaired," and "moderately impaired" conditions, respectively. We conclude that data from the MK2 is of sufficient quality to perform objective motor behavior classification in individuals with upper limb impairment. The data collection and analysis framework that we have developed has the potential to disrupt the field of clinical assessment. Future studies will focus on validating this protocol on large populations of individuals with actual upper limb impairments in order to create a toolkit that is clinically validated and available to the clinical community.

International audience; Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD) matrices has proved of great utility in applications requiring interpolation, extrapolation, smoothing, signal detection and classification. The power means of SPD matrices with exponent p in the interval [-1, 1] interpolate in between the Harmonic mean (p =-1) and the Arithmetic mean (p = 1), while the Geometric (Cartan/Karcher) mean, which is the one currently employed in most applications, corresponds to their limit evaluated at 0. In this article we treat the problem of estimating power means along the continuum p(-1, 1) given noisy observed measurement. We provide a general fixed point algorithm (MPM) and we show that its convergence rate for p = ±0.5 deteriorates very little with the number and dimension of points given as input. Along the whole continuum, MPM is also robust with respect to the dispersion of the points on the manifold (noise), much more so than the gradient descent algorithm usually employed to estimate the geometric mean. Thus, MPM is an efficient algorithm for the whole family of power means, including the geometric mean, which by MPM can be approximated with a desired precision by interpolating two solutions obtained with a small ±p value. We also present an approximated version of the MPM algorithm with very low computational complexity for the special case p=±½. Finally, we show the appeal of power means through the classification of brain-computer interface event-related potentials data.

Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2649006 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 25 subjects testing the Brain Invaders (Congedo, 2011), a visual P300 Brain-Computer Interface inspired by the famous vintage video game Space Invaders (Taito, Tokyo, Japan). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. EEG data were recorded by 16 electrodes in an experiment that took place in the GIPSA-lab, Grenoble, France, in 2012 (Van Veen, 2013 and Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2012-GIPSA. The ID of this dataset is BI.EEG.2012-GIPSA., arXiv admin note: substantial text overlap with arXiv:1904.09111

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2649006 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 25 subjects testing the Brain Invaders (Congedo, 2011), a visual P300 Brain-Computer Interface inspired by the famous vintage video game Space Invaders (Taito, Tokyo, Japan). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. EEG data were recorded by 16 electrodes in an experiment that took place in the GIPSA-lab, Grenoble, France, in 2012 (Van Veen, 2013 and Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2012-GIPSA. The ID of this dataset is BI.EEG.2012-GIPSA.; Dans ce document, nous décrivons une expérimentation dont les données ont été publiées à https://doi.org/10.5281/zenodo.2649006 aux formats mat et csv. Ce jeu de donnée contient les enregistrements électroencéphalographiques (EEG) de 25 sujets testant Brain Invaders (Congedo, 2011), une interface cerveau-ordinateur de type ‘P300 visuel’ inspirée du fameux jeu vintage Space Invaders (Taito, Tokyo, Japan). Le P300 visuel est une perturbation du signal EEG apparaissant 240-600 ms après le début d'une stimulation visuelle. L'EEG de chaque sujet a été enregistré grâce à 16 électrodes réparties sur la surface du scalp. L'expérience a été menée au GIPSA-lab (Université de Grenoble-Alpes, CNRS, Grenoble-INP) en 2012 (Van Veen, 2013 et Congedo, 2013). Nous fournissons également une implémentation python pour manipuler les données à https://github.com/plcrodrigues/py.BI.EEG.2012-GIPSA. L’identifiant de cette base de données est BI.EEG.2012-GIPSA.

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.1494163 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. The ID of this dataset is BI.EEG.2013-GIPSA.; Dans ce document, nous décrivons une expérimentation dont les données ont été publiées sur https://doi.org/10.5281/zenodo.1494163 aux formats mat et csv. Ce jeu de donnée contient les enregistrements électroencéphalographiques (EEG) de 24 sujets durant une expérience sur les interfaces cerveau-ordinateur de type ‘P300 visuel’. Le P300 visuel est une perturbation du signal EEG apparaissant 240-600 ms après le début d'une stimulation visuelle. Le but de cette expérience était de comparer l'utilisation d'une interface cerveau-machine (ICM) basée sur le P300, sous PC, avec et sans calibration adaptive en utilisant la géométrie Riemannienne. L'EEG de chaque sujet a été enregistré grâce à 16 électrodes réparties sur la surface du scalp. L'expérience a été menée au GIPSA-lab (Université de Grenoble-Alpes, CNRS, Grenoble-INP) en 2013 (Congedo, 2013). Nous fournissons également une implémentation python pour manipuler les données à https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. L’identifiant de cette base de données est BI.EEG.2013-GIPSA.

Current methods of balance assessment in the clinical environment are often subjective, time-consuming and lack clinical relevance for non-ambulatory older adults. The objective of this study was to develop a novel method of balance assessment that utilizes data collected using the Microsoft Kinect 2 to create a Berg Balance Scale score, which is completely determined by statistical methods rather than by human evaluators. 74 older adults, both healthy and balance impaired, were recruited for this trial. All participants completed the Berg Balance Scale (BBS) which was scored independently by trained physical therapists. Participants then completed the items of the “Modified Berg Balance Scale” in front of the Microsoft Kinect camera. Kinematic data collected during this measurement was used to train a feed-forward neural network that was used to assign a Berg Balance Scale score. The neural network model estimated the clinician-assigned BBS score to within a median of 0.93 points for the participants in our sample population (range: 0.02–5.69). Using low-cost depth sensing camera technology and a clinical protocol that takes less than 5 min to complete in both ambulatory and non-ambulatory older adults, the method outlined in this manuscript can accurately predict a participant’s BBS score and thereby identify whether they are deemed a high fall risk or not. If implemented correctly, this could enable fall prevention services to be deployed in a timely fashion using low-cost, accessible technology, resulting in improved safety of older adults.

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.1494163 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. The ID of this dataset is BI.EEG.2013-GIPSA.

A central challenge to cognitive neuroscience consists in decomposing complex brain signals into an interpretable sequence of operations-an algorithm-which ultimately accounts for intelligent behaviors. Over the past decades, a variety of analytical tools have been developed to (i) isolate each algorithmic step and (ii) track their ordering from neuronal activity. In the present chapter, we briefly review the main methods to encode and decode temporally-resolved neural recordings, show how these approaches relate to one-another, and summarize their main premises and challenges. Finally we highlight, through a series of recent findings, the increasing role of machine learning both as i) a method to extract convoluted patterns of neural activity, and as ii) an operational framework to formalize the computational bases of cognition. Overall, we discuss how modern analyses of neural time series can identify the algorithmic organization of cognition.

International audience; To better understand the relationship between EEG signals and hand movements the WAY Consortium has organized the Grasp-and-Lift EEG Detection challenge. It was held in 2015 from 29th June to 31th August on Kaggle, a platform for competitive predictive modeling, and attracted 379 contesting teams. The goal of the challenge was to detect 6 different events related to hand movement during a task of grasping and lifting an object, using only EEG signal. The 6 events were representing different stages of a sequence of hand movements (hand starts moving, starts lifting the object, etc.). True labels were extracted from EMG signal, and provided as a +/-150ms frame centered on the occurrence of the event. Contestants were asked to provide probabilities of detection for the 6 events and for every time sample. The evaluation metric for this challenge was the Area Under the ROC Curve (AUC) averaged over the 6 event types. Finally, the model must be causal, i.e. only the data from the past can be used to predict the events. This abstract presents the winning solution of this challenge.

Representational similarity analysis (RSA) is a popular technique to estimate the structure of mental representations from neuroimaging data. However, RSA can be difficult to estimate for neural time series, where mental representations may be distributed in a highly dimensional space. Here, we show that RSA can be efficiently estimated from dense neural time series using Riemannian geometry. Using a public magneto-encephalography dataset, we decoded 24 classes from the brain evoked responses to 720 visual stimuli. RSA estimated from the confusion matrices of a standard regularized logistic regression achieved an average decoding accuracy of 23% (chance=4%). Our approach based on spatial filtering and Riemannian geometry nearly doubled this score with an average 42% decoding accuracy. Finally, our results revealed how RSA becomes ill-conceived when it derives from confusion matrices of highly accurate multivariate pattern classifications. Instead, we propose to directly estimate RSA from Riemannian metrics without fitting a multivariate pattern classifier. Overall, our approach, based on Riemannian geometry provides a principled and efficient basis to study the structure of mental representations from highly dimensional neural time series.

The recognition of brain evoked responses at the single-trial level is a challenging task. Typical non-invasive brain-computer interfaces based on event-related brain responses use eletroencephalograhy. In this study, we consider brain signals recorded with magnetoencephalography (MEG), and we expect to take advantage of the high spatial and temporal resolution for the detection of targets in a series of images. This study was used for the data analysis competition held in the 20th International Conference on Biomagnetism (Biomag) 2016, wherein the goal was to provide a method for single-trial detection of even-related fields corresponding to the presentation of happy faces during the rapid presentation of images of faces with six different facial expressions (anger, disgust, fear, neutrality, sadness, and happiness). The datasets correspond to 204 gradiometers signals obtained from four participants. The best method is based on the combination of several approaches, and mainly based on Riemannian geometry, and it provided an area under the ROC curve of 0.956±0.043. The results show that a high recognition rate of facial expressions can be obtained at the signal-trial level using advanced signal processing and machine learning methodologies.

International audience; Riemannian geometry has been found accurate and robust for classifying multidimensional data, for instance, in brain-computer interfaces based on electroencephalography. Given a number of data points on the manifold of symmetric positive-definite matrices, it is often of interest to embed these points in a manifold of smaller dimension. This is necessary for large dimensions in order to preserve accuracy and useful in general to speed up computations. Geometry-aware methods try to accomplish this task while respecting as much as possible the geometry of the original data points. We provide a closed-form solution for this problem in a fully unsupervised setting. Through the analysis of three brain-computer interface data bases we show that our method allows substantial dimensionality reduction without affecting the classification accuracy.

We tested the hypothesis that somatosensory system injury would more strongly affect movement than motor system injury in children with unilateral cerebral palsy (USCP). This hypothesis was based on how somatosensory and corticospinal circuits adapt to injury during development; whereas the motor system can maintain connections to the impaired hand from the uninjured hemisphere, this does not occur in the somatosensory system. As a corollary, cortical injury strongly impairs sensory function, so we hypothesized that cortical lesions would impair hand function more than subcortical lesions.Twenty-four children with unilateral cerebral palsy had physiological and anatomical measures of the motor and somatosensory systems and lesion classification. Motor physiology was performed with transcranial magnetic stimulation and somatosensory physiology with vibration-evoked electroencephalographic potentials. Tractography of the corticospinal tract and the medial lemniscus was performed with diffusion tensor imaging, and lesions were classified by magnetic resonance imaging. Anatomical and physiological results were correlated with measures of hand function using 2 independent statistical methods.Children with disruptions in the somatosensory connectivity and cortical lesions had the most severe upper extremity impairments, particularly somatosensory function. Motor system connectivity was significantly correlated with bimanual function, but not unimanual function or somatosensory function.Both sensory and motor connectivity impact hand function in children with USCP. Somatosensory connectivity could be an important target for recovery of hand function in children with USCP. Ann Neurol 2017;82:766-780.

International audience; Despite its short history, the use of Riemannian geometry in brain-computer interface (BCI) decoding is currently attracting increasing attention, due to accumulating documentation of its simplicity, accuracy, robustness and transfer learning capabilities, including the winning score obtained in five recent international predictive modeling BCI data competitions. The Riemannian framework is sharp from a mathematical perspective, yet in practice it is simple, both algorithmically and computationally. This allows the conception of online decoding machines suiting real-world operation in adverse conditions. We provide here a review on the use of Riemannian geometry for BCI and a primer on the classification frameworks based on it. While the theoretical research on Riemannian geometry is technical, our aim here is to show the appeal of the framework on an intuitive geometrical ground. In particular, we provide a rationale for its robustness and transfer learning capabilities and we elucidate the link between a simple Riemannian classifier and a state-of-the-art spatial filtering approach. We conclude by reporting details on the construction of data points to be manipulated in the Riemannian framework in the context of BCI and by providing links to available open-source Matlab and Python code libraries for designing BCI decoders.

The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: "both feet on the ground" (1), "One foot off the ground" (2), and "both feet off the ground" (3). We compared force platform and MK2 data to quantify the degree to which the MK2 was returning reliable data across subjects. We then applied a novel machine-learning paradigm to the MK2 data in order to determine the extent to which data from the MK2 could be used to reliably classify different postural conditions. Our initial comparison of force plate and MK2 data showed a strong agreement between the two devices, with strong Pearson correlations between the trunk centroids "Spine_Mid" (0.85 ± 0.06), "Neck" (0.86 ± 0.07) and "Head" (0.87 ± 0.07), and the center of pressure centroid inferred by the force platform. Mean accuracy for the machine learning classifier from MK2 was 97.0%, with a specific classification accuracy breakdown of 90.9%, 100%, and 100% for conditions 1 through 3, respectively. Mean accuracy for the machine learning classifier derived from the force platform data was lower at 84.4%. We conclude that data from the MK2 has sufficient information content to allow us to classify sequences of tasks being performed under different levels of postural stability. Future studies will focus on validating this protocol on large populations of individuals with actual balance impairments in order to create a toolkit that is clinically validated and available to the medical community.

International audience; This 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.

International audience; The 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 .

International audience; Introduction: 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.

Objective Brain-computer interface (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. Approach 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. Main results We analyze a set of state-of-the-art decoding algorithms across 12 open access datasets, including over 250 subjects. Our results show that even for the best methods, there are datasets which do not show significant improvements, and further that many previously validated methods do not generalize well outside the datasets they were tested on. Significance Our analysis confirms that BCI algorithms validated on single datasets are not representative, highlighting the need for more robust validation in the machine learning for BCIs community.

This study presents the outcome of the 5-year-long French national project aiming at the development and evaluation of an effective brain-computer interface (BCI) prototype for the communication of patients with acute motor disabilities. It presents results from two clinical studies: a clinical feasibility study carried out partly in the intensive care unit (ICU) and the clinical evaluation of an innovative BCI prototype. In this second study the BCI performance of patients was compared to that of healthy volunteers and benchmarked against a traditional assistive technology (scanning device). Altogether, 15 of 22 patients could control the BCI system with an accuracy significantly above the chance level. The bit-rate of the traditional assistive technology proved superior, even though an equivalent bit-rate could be achieved using personalized parameters for the BCI. Fatigue was found to be the primary limitation factor, which was particularly true for patients and during the use of the BCI. A cla...

National audience; Cet article présente une nouvelle méthode de classification pour les potentiels évoqués dans le cadre des interfaces cerveau machine (ICMs) EEG. À travers une estimation spécifique des matrices de covariance, ce travail étend l'utilisation de la géométrie Riemannienne, jusqu'alors limitée aux ICMs fondées sur l'imagination motrice, à la classification des potentiel évoqués. En comparaison aux méthodes de l'état de l'art, la méthode présentée offre une augmentation des performances tout en diminuant le nombre de données de nécessaire à la calibration du classifieur et en offrant une meilleure généralisation entre les sessions d'enregistrement.

International audience; A methodology is presented to address the issue of detecting Error Potentials in the context of a P300-speller brain-computer interface.The proposed approach proved highly efficient in a recent competition organized on kaggle.com: authors finished first out of 260 competingteams (311 participants). The approach consists of three main foundations: an ad-hoc spatial filtering focuses the energy of the evoked potential,covariance matrices of the EEG signals are then interpreted in a Riemannian fashion to build features fed to the classification algorithm, andlastly, a bagging procedure increases robustness when detecting Error Potentials on unseen subjects.; Cette communication présente une méthode de détection de potentiels d'erreur dans le cadre d'une interface cerveau-machine P300-speller. Cette méthode a été employée avec succès lors d'une compétition ayant rassemblé 260 équipes (311 personnes) sur kaggle.com. L'approche repose sur trois pilliers principaux : un filtrage spatial concentrant le signal d'intérêt, une classification basée sur des caractéristiques issues d'une interprétation Riemannienne des matrices de covariance du signal électroencéphalographique, et enfin une méthode d'ensemble (bagging) apportant la robustesse nécessaire au transfert des performances pour la détection des potentiels d'erreur sur de nouveaux sujets.

International audience; We explore the connection between two problems that have arisen independently in the signal processing and related fields: the estimation of the geometric mean of a set of symmetric positive definite (SPD) matrices and their approximate joint diagonalization (AJD). Today there is a considerable interest in estimating the geometric mean of a SPD matrix set in the manifold of SPD matrices endowed with the Fisher information metric. The resulting mean has several important invariance properties and has proven very useful in diverse engineering applications such as biomedical and image data processing. While for two SPD matrices the mean has an algebraic closed form solution, for a set of more than two SPD matrices it can only be estimated by iterative algorithms. However, none of the existing iterative algorithms feature at the same time fast convergence, low computational complexity per iteration and guarantee of convergence. For this reason, recently other definitions of geometric mean based on symmetric divergence measures, such as the Bhattacharyya divergence, have been considered. The resulting means, although possibly useful in practice, do not satisfy all desirable invariance properties. In this paper we consider geometric means of co-variance matrices estimated on high-dimensional time-series, assuming that the data is generated according to an instantaneous mixing model, which is very common in signal processing. We show that in these circumstances we can approximate the Fisher information geometric mean by employing an efficient AJD algorithm. Our approximation is in general much closer to the Fisher information geometric mean as compared to its competitors and verifies many invariance properties. Furthermore, convergence is guaranteed, the computational complexity is low and the convergence rate is quadratic. The accuracy of this new geometric mean approximation is demonstrated by means of simulations.

International audience; The use of spatial covariance matrix as feature is investigated for motor imagery EEG-based classification. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms significantly state of the art results without the need for spatial filtering.

International audience; This 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.

International audience; This paper addresses the issue of asynchronous brain-switch. The detection of a specific brain pattern from the ongoing EEG activity is achieved by using the Riemannian geometry, which offers an interesting framework for EEG mental task classification, and is based on the fact that spatial covariance matrices obtained on short-time EEG segments contain all the desired information. Such a brain-switch is valuable as it is easy to set up and robust to artefacts. The performances are evaluated offline using EEG recordings collected on 6 subjects in our laboratory. The results show a good precision (Positive Predictive Value) of 92% with a sensitivity (True Positive Rate) of 91%.

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.

Currently the Riemannian geometry of symmetric positive definite (SPD) matrices is gaining momentum as a powerful tool in a wide range of engineering applications such as image, radar and biomedical data signal processing. If the data is not natively represented in the form of SPD matrices, typically we may summarize them in such form by estimating covariance matrices of the data. However once we manipulate such covariance matrices on the Riemannian manifold we lose the representation in the original data space. For instance, we can evaluate the geometric mean of a set of covariance matrices, but not the geometric mean of the data generating the covariance matrices, the space of interest in which the geometric mean can be interpreted. As a consequence, Riemannian information geometry is often perceived by non-experts as a “black-box” tool and this perception prevents a wider adoption in the scientific community. Hereby we show that we can overcome this limitation by constructing a special form of SPD matrix embedding both the covariance structure of the data and the data itself. Incidentally, whenever the original data can be represented in the form of a generic data matrix (not even square), this special SPD matrix enables an exhaustive and unique description of the data up to second-order statistics. This is achieved embedding the covariance structure of both the rows and columns of the data matrix, allowing naturally a wide range of possible applications and bringing us over and above just an interpretability issue. We demonstrate the method by manipulating satellite images (pansharpening) and event-related potentials (ERPs) of an electroencephalography brain-computer interface (BCI) study. The first example illustrates the effect of moving along geodesics in the original data space and the second provides a novel estimation of ERP average (geometric mean), showing that, in contrast to the usual arithmetic mean, this estimation is robust to outliers. In conclusion, we are able to show that the Riemannian concepts of distance, geometric mean, moving along a geodesic, etc. can be readily transposed into a generic data space, whatever this data space represents.

This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. Through a new estimation of covariance matrices, this work extend the use of Riemannian geometry, which was previously limited to SMR-based BCI, to the problem of classification of ERPs. As compared to the state-of-the-art, this new method increases performance, reduces the number of data needed for the calibration and features good generalisation across sessions and subjects. This method is illustrated on data recorded with the P300-based game brain invaders. Finally, an online and adaptive implementation is described, where the BCI is initialized with generic parameters derived from a database and continuously adapt to the individual, allowing the user to play the game without any calibration while keeping a high accuracy.

This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. Through a new estimation of covariance matrices, this work extend the use of Riemannian geometry, which was previously limited to SMR-based BCI, to the problem of classification of ERPs. As compared to the state-of-the-art, this new method increases performance, reduces the number of data needed for the calibration and features good generalisation across sessions and subjects. This method is illustrated on data recorded with the P300-based game brain invaders. Finally, an online and adaptive implementation is described, where the BCI is initialized with generic parameters derived from a database and continuously adapt to the individual, allowing the user to play the game without any calibration while keeping a high accuracy.

33 pages, 9 Figures, 17 equations/algorithms. Rapport interne de GIPSA-lab; 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.

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/algorithms

International audience; This paper presents a new classification framework for brain-computer interface (BCI) based on motor imagery. This framework involves the concept of Riemannian geometry in the manifold of covariance matrices. The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these matrices using the topology of the manifold of symmetric and positive definite (SPD) matrices. This framework allows to extract the spatial information contained in EEG signals without using spatial filtering. Two methods are proposed and compared with a reference method [multiclass Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA)] on the multiclass dataset IIa from the BCI Competition IV. The first method, named minimum distance to Riemannian mean (MDRM), is an implementation of the minimum distance to mean (MDM) classification algorithm using Riemannian distance and Riemannian mean. This simple method shows comparable results with the reference method. The second method, named tangent space LDA (TSLDA), maps the covariance matrices onto the Riemannian tangent space where matrices can be vectorized and treated as Euclidean objects. Then, a variable selection procedure is applied in order to decrease dimensionality and a classification by LDA is performed. This latter method outperforms the reference method increasing the mean classification accuracy from 65.1% to 70.2%.

International audience; This paper presents a link between the well known Common Spatial Pattern (CSP) algorithm and Riemannian geometry in the context of Brain Computer Interface (BCI). It will be shown that CSP spatial filtering and Log variance features extraction can be resumed as a computation of a Riemann distance in the space of covariances matrices. This fact yields to highlight several approximations with respect to the space topology. According to these conclusions, we propose an improvement of classical CSP method.

ISBN 978-3-642-15994-7, Softcover; International audience; In brain-computer interfaces based on motor imagery, covariance matrices are widely used through spatial filters computation and other signal processing methods. Covariance matrices lie in the space of Symmetric Positives-Definite (SPD) matrices and therefore, fall within the Riemannian geometry domain. Using a differential geometry framework, we propose different algorithms in order to classify covariance matrices in their native space.

ISBN : 978-2-9532965-0-1; Common 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.

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.1494163 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. The ID of this dataset is BI.EEG.2013-GIPSA.; Dans ce document, nous décrivons une expérimentation dont les données ont été publiées sur https://doi.org/10.5281/zenodo.1494163 aux formats mat et csv. Ce jeu de donnée contient les enregistrements électroencéphalographiques (EEG) de 24 sujets durant une expérience sur les interfaces cerveau-ordinateur de type ‘P300 visuel’. Le P300 visuel est une perturbation du signal EEG apparaissant 240-600 ms après le début d'une stimulation visuelle. Le but de cette expérience était de comparer l'utilisation d'une interface cerveau-machine (ICM) basée sur le P300, sous PC, avec et sans calibration adaptive en utilisant la géométrie Riemannienne. L'EEG de chaque sujet a été enregistré grâce à 16 électrodes réparties sur la surface du scalp. L'expérience a été menée au GIPSA-lab (Université de Grenoble-Alpes, CNRS, Grenoble-INP) en 2013 (Congedo, 2013). Nous fournissons également une implémentation python pour manipuler les données à https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. L’identifiant de cette base de données est BI.EEG.2013-GIPSA.