Analyse discriminante matricielle descriptive. Application a l'étude de signaux EEG

National audienceWe focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determined by the singular value decomposition of the difference of the class-averages with the Mahalanobis metric in the row and column spaces. This approach provides data representations of data in two-dimensional or three-dimensional plots and singles out discriminant components. An application to electroencephalographic multi-sensor signals illustrates the relevance of the method.Nous nous intéressons à l'approche descriptive de l'analyse discriminante linéaire de données matricielles dans le cas binaire. Sous l'hypothèse de séparabilité de la variabilité des lignes de celle des colonnes, les combinaisons linéaires des lignes et des colonnes les plus discriminantes sont déterminées par la décomposition en valeurs singulières de la différence des moyennes des deux classes en munissant les espaces des lignes et des colonnes de la métrique de Mahalanobis. Cette approche permet d'obtenir des représentations des données dans des plans factoriels et de dégager des composantes discriminantes. Une application a des signaux d'électroencéphalographie multi-capteurs illustre la pertinence de la méthode

Analyse discriminante matricielle descriptive. Application a l'\'etude de signaux EEG

We focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determined by the singular value decomposition of the difference of the class-averages with the Mahalanobis metric in the row and column spaces. This approach provides data representations of data in two-dimensional or three-dimensional plots and singles out discriminant components. An application to electroencephalographic multi-sensor signals illustrates the relevance of the method.Comment: in French, Journ{\'e}es de statistique de la SFDS, Jun 2015, Lille, Franc

Does function fit structure? A ground truth for non-invasive neuroimaging.

There are now a number of non-invasive methods to image human brain function in-vivo. However, the accuracy of these images remains unknown and can currently only be estimated through the use of invasive recordings to generate a functional ground truth. Neuronal activity follows grey matter structure and accurate estimates of neuronal activity will have stronger support from accurate generative models of anatomy. Here we introduce a general framework that, for the first time, enables the spatial distortion of a functional brain image to be estimated empirically. We use a spherical harmonic decomposition to modulate each cortical hemisphere from its original form towards progressively simpler structures, ending in an ellipsoid. Functional estimates that are not supported by the simpler cortical structures have less inherent spatial distortion. This method allows us to compare directly between magnetoencephalography (MEG) source reconstructions based upon different assumption sets without recourse to functional ground truth

A user-centred approach to unlock the potential of non-invasive BCIs: an unprecedented international translational effort

Non-invasive Mental Task-based Brain-Computer Interfaces (MT-BCIs) enable their users to interact with the environment through their brain activity alone (measured using electroencephalography for example), by performing mental tasks such as mental calculation or motor imagery. Current developments in technology hint at a wide range of possible applications, both in the clinical and non-clinical domains. MT-BCIs can be used to control (neuro)prostheses or interact with video games, among many other applications. They can also be used to restore cognitive and motor abilities for stroke rehabilitation, or even improve athletic performance.Nonetheless, the expected transfer of MT-BCIs from the lab to the marketplace will be greatly impeded if all resources are allocated to technological aspects alone. We cannot neglect the Human End-User that sits in the centre of the loop. Indeed, self-regulating one’s brain activity through mental tasks to interact is an acquired skill that requires appropriate training. Yet several studies have shown that current training procedures do not enable MT-BCI users to reach adequate levels of performance. Therefore, one significant challenge for the community is that of improving end-user training.To do so, another fundamental challenge must be taken into account: we need to understand the processes that underlie MT-BCI performance and user learning. It is currently estimated that 10 to 30% of people cannot control an MT-BCI. These people are often referred to as “BCI inefficient”. But the concept of “BCI inefficiency” is debated. Does it really exist? Or, are low performances due to insufficient training, training procedures that are unsuited to these users or is the BCI data processing not sensitive enough? The currently available literature does not allow for a definitive answer to these questions as most published studies either include a limited number of participants (i.e., 10 to 20 participants) and/or training sessions (i.e., 1 or 2). We still have very little insight into what the MT-BCI learning curve looks like, and into which factors (including both user-related and machine-related factors) influence this learning curve. Finding answers will require a large number of experiments, involving a large number of participants taking part in multiple training sessions. It is not feasible for one research lab or even a small consortium to undertake such experiments alone. Therefore, an unprecedented coordinated effort from the research community is necessary.We are convinced that combining forces will allow us to characterise in detail MT-BCI user learning, and thereby provide a mandatory step toward transferring BCIs “out of the lab”. This is why we gathered an international, interdisciplinary consortium of BCI researchers from more than 20 different labs across Europe and Japan, including pioneers in the field. This collaboration will enable us to collect considerable amounts of data (at least 100 participants for 20 training sessions each) and establish a large open database. Based on this precious resource, we could then lead sound analyses to answer the previously mentioned questions. Using this data, our consortium could offer solutions on how to improve MT-BCI training procedures using innovative approaches (e.g., personalisation using intelligent tutoring systems) and technologies (e.g., virtual reality). The CHIST-ERA programme represents a unique opportunity to conduct this ambitious project, which will foster innovation in our field and strengthen our community

Méthodes adaptatives en apprentissage machine

International audienceLes signaux utilisés dans le cadre des BCI non-invasives (EEG, MEG, ...) varient fortement au cours du temps chez un même sujet, à la fois entre les différentes sessions d'utilisation, mais également au sein d'une même session, en fonction de la fatigue ou de la motivation du sujet, ou de changements matériels, comme la position des capteurs et leur impédance.Les méthodes adaptatives visent à traiter le problème de la \textit{non-stationnarité} du signal, ou, plus précisément, de la variabilité de non-intérêt (voire parasitaire), par opposition à la variabilité d'intérêt relative notamment aux différents degrés de liberté de l'interface.Ces méthodes adaptatives consistent à modifier la fonction de réponse en cours d'utilisation, afin que les performances de l'interface soient maintenues (voire améliorées) au cours de leur utilisation

Le retour biaisé influence l’apprentissage de l'imagerie motrice d'ICO

International audienceVarious user trainings were proposed to assist the user in accomplishing Motor Imagery (MI) BCI task, e.g. the use of positive (biased) feedback which is an optimistic representation of one's labeled brain activity has shown to increase performance or learning. On the contrary, in some cases positive feedback decreased while negative increased user learning within one session. In order to better understand the benefits of biased feedback on performance and learning during the BCI training, we consider user states such as workload and the flow state, a state of optimal cognitive control, immersion, and pleasure, which has shown to correlate to performance

Mind the Traps! Design Guidelines for Rigorous BCI Experiments

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Active Inference for Adaptive BCI: application to the P300 Speller

International audienceAdaptive Brain-Computer interfaces (BCIs) have shown to improve performance, however a general and flexible framework to implement adaptive features is still lacking. We appeal to a generic Bayesian approach, called Active Inference (AI), to infer user's intentions or states and act in a way that optimizes performance. In realistic P300-speller simulations, AI outperforms traditional algorithms with an increase in bit rate between 18% and 59%, while offering a possibility of unifying various adaptive implementations within one generic framework

Tactile Hypersensitivity and GABA Concentration in the Sensorimotor Cortex of Adults with Autism

International audienceSensory hypersensitivity is frequently encountered in autism spectrum disorder (ASD). Gamma-aminobutyric acid (GABA) has been hypothesized to play a role in tactile hypersensitivity. The aim of the present study was twofold. First, as a study showed that children with ASD have decreased GABA concentrations in the sensorimotor cortex, we aimed at determining whether the GABA reduction remained in adults with ASD. For this purpose, we used magnetic resonance spectroscopy to measure GABA concentration in the sensorimotor cortex of neurotypical adults (n = 19) and ASD adults (n = 18). Second, we aimed at characterizing correlations between GABA concentration and tactile hypersensitivity in ASD. GABA concentration in the sensorimotor cortex of adults with ASD was lower than in neurotypical adults (decrease by 17%). Interestingly, GABA concentrations were positively correlated with self-reported tactile hypersensitivity in adults with ASD (r = 0.50, P = 0.01), but not in neurotypical adults. In addition, GABA concentrations were negatively correlated with the intra-individual variation during threshold measurement, both in neurotypical adults (r = −0.47, P = 0.04) and in adults with ASD (r = −0.59, P = 0.01). In other words, in both groups, the higher the GABA level, the more precise the tactile sensation. These results highlight the key role of GABA in tactile sensitivity, and suggest that atypical GABA modulation contributes to tactile hypersensitivity in ASD. We discuss the hypothesis that hypersensitivity in ASD could be due to suboptimal predictions about sensations. Autism Research 2019. Lay Summary: People with autism spectrum disorder (ASD) often experience tactile hypersensitivity. Here, our goal was to highlight a link between tactile hypersensitivity and the concentration of gamma-aminobutyric acid (GABA) (an inhibitory neurotransmitter) in the brain of adults with ASD. Indeed, self-reported hypersensitivity correlated with reduced GABA levels in brain areas processing touch. Our study suggests that this neurotransmitter may play a key role in tactile hypersensitivity in autism