I am an Inria research scientist at Paris Brain Institute in the ARAMIS Lab.
My research currently focuses on the development of tools to address the “Brain-Computer Interface (BCI) inefficiency” issue, reflected by a non-negligible portion of users who cannot control the device even after several training sessions. I essentially consider two main approaches: the search for neurophysiological markers of BCI training and the integration of multimodal data to enrich the information provided to the classifier.
I serve as secretary general of the French academic association promoting the advances in BCI, called CORTICO, and as co-chair of the Postdocs and Students Committee of the BCI Society.
You can download my CV in pdf.
Don’t hesitate to contact me if you want any additional information or if you are interested by a research collaboration!
PhD in Biomedical instrumentation, 2015
CEA-LETI (Grenoble, France)
MSc in Neuropsychology and Clinical Neurosciences, 2015
Grenoble Alpes University
MEng in Information and Communications Technology for Health, 2012
IMT Atlantique (Brest, France)
This is going to be a great edition with:
one workshop on challenges in BCI-based neurofeedback applications for neurological disorders - co-organized with Fabien Lotte, Camille Jeunet, Nathalie George, Fabrizio De Vico Fallani, and Lin Yao
one workshop on offline and onine tools for real-world BCI applications - co-organized with Sylvain chevallier, Pierre Clisson, Pedro Rodrigues, and Arthur Desbois
one poster on the FUCONE approach
three lunches with mentors - co-organized with the Postdocs and Students Committee of the BCI Society
The next CORTICO days will take place in May 9-10 at Paris Brain Institute! With Sylvain Chevallier we are more than delighted to organize this event. Two exceptional keynotes will be given by Mario Chavez and Donatella Mattia!
Interested in participating?
👉 Here is the program
👉 Here is the link to register
Young researcher will have to opportunity to present their work on BCI to get feedbacks from experts? Please consider submitting an abstract!
I had the priviledge to present my work on BCI and to discuss with the attendance on career paths in the academia. A huge thank you to the organizers of this Paris Pyladies event!
Materials used during the talk can be found here.
Functional connectivity and brain network reorganization underlying longitudinal processes, mainly BCI training
Development of methods to enhance subjets’ mental state classification. They can be divided in two main approaches: the integration of multimodal information and the search for alternative features
Development of cryogenic-free sensors for magnetocardiography and magnetoencephalography
The reconfiguration of large-scale interactions among multiple brain regions underpins complex behavior. It manifests in bursts of activations, called neuronal avalanches, which can be tracked non-invasively as they expand across the brain. Responding to a new task requires brain regions to appropriately reconfigure their interactions, which might in turn affect the path of propagation of neuronal avalanches, providing a readily-accessible read out of the processes sustaining behavior. As such, neuronal avalanche measures constitute natural candidates to design brain-computer interfaces. To test this hypothesis, we used source-reconstructed magneto/electroencephalography, comparing resting-state to motor imagery conditions during a BCI protocol. For each experimental condition, we computed an individual avalanche transition matrix, to track the probability that an avalanche would spread across any two regions. Then, we selected those edges whose transition probabilities significantly differed between conditions, at the individual level and in the majority of the participants. We found a robust topography of the edges that were affected by the execution of the task, which mainly hinge upon the premotor regions. Furthermore, we related the individual differences to the task performance, showing that significant correlations are predominantly positive and involve edges connecting pre/motor regions to parietal ones. Our results show that the pattern of propagation of large-scale perturbations are related to behavior, and that they can be used to optimize brain-computer interfaces.
In this chapter, we present the main characteristics of electroencephalography (EEG) and magnetoencephalography (MEG). More specifically, this chapter is dedicated to the presentation of the data, the way they can be acquired and analyzed. Then, we present the main features that can be extracted and their applications for brain disorders with concrete examples to illustrate them. Additional materials associated with this chapter are available in the dedicated Github repository.
Objective: Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery. Methods: A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets. Results: Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods. Conclusion: The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability. Significance: Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.
Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this framework to functional connectivity measures. This paper describes the approach submitted to the Clinical BCI Challenge-WCCI2020 and that ranked 1st on the task 1 of the competition.
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.
Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.
We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (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 noninvasive BCIs.