Marie-Constance Corsi

Marie-Constance Corsi
National Institute for Research in Computer Science and Control | INRIA · ARAMIS - Algorithmes, Modèles et Méthodes pour les Images et les Signaux du Cerveau Humain Sain et Pathologique Research Team

PhD

About

32
Publications
6,139
Reads
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303
Citations
Additional affiliations
May 2016 - present
L'Institut du Cerveau et de la Moelle Épinière
Position
  • PostDoc Position
October 2012 - October 2015
Atomic Energy and Alternative Energies Commission
Position
  • PhD Student
Education
September 2014 - September 2015
Université Grenoble Alpes
Field of study
  • Neuropsychology and Clinical neurosciences
October 2012 - October 2015
Université Grenoble Alpes
Field of study
  • Biomedical instrumentation
September 2009 - October 2012
IMT Atlantique
Field of study
  • Information and communications technology, signal processing, biophysics

Publications

Publications (32)
Article
Full-text available
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 perf...
Article
In this paper, we present the first proof of concept confirming the possibility to record magnetoencephalographic (MEG) signals with Optically Pumped Magnetometers (OPMs) based on the parametric resonance of 4He atoms. The main advantage of this kind of OPM is the possibility to provide a tri-axis vector measurement of the magnetic field at room-te...
Article
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 simultaneo...
Article
Full-text available
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 remain...
Article
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 class...
Preprint
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...
Article
Brain-computer interfaces allow interactions based on brain activities detected in electroencephalography. Despite important improvements in the last decade, some subjects still achieve poor performances without any identified cause. On the one hand, State-of-the-art methods for online decoding are based on covariance matrices seen as elements of a...
Preprint
Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and that is mostly applied for brain activity analysis. Building on the advances in information geometry for brain-computer interface, we propose a novel framework that comb...
Conference Paper
In the recent years, brain computer interfaces (BCI) using motor imagery have shown some limitations regarding the quality of control. In an effort to improve this promising technology, some studies intended to develop hybrid BCI with other technologies such as eye tracking which shows more reliability. However, the use of an eye tracker in the con...
Article
Functional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detection in brain-computer interfaces (BCIs). However, errors in the estimation of connectivity can affect the detection performances. In this work, we address the problem of...
Article
Full-text available
In the last decade, functional connectivity (FC) has been increasingly adopted based on its ability to capture statistical dependencies between multivariate brain signals. However, the role of FC in the context of brain-computer interface applications is still poorly understood. To address this gap in knowledge, we considered a group of 20 healthy...
Article
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on ¹⁸F-florbetapir and ¹⁸F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive feature...
Preprint
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies on Riemannian Geometry, with a twist. Instead of using the classical covariance matrices, we also rely on measures of functional connectivity. Our approach ranked 1st on...
Preprint
Functional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detection in brain-computer interfaces (BCIs). However, errors in the estimation of connectivity can affect the detection performances. In this work, we address the problem of...
Article
Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease (AD) (Jack et al., 2018). Electroencephalography (EEG) is a non‐invasive and cheap technique that would be an interesting screening tool for the preclinical stage of AD. We included participants from the INSIGHT‐preAD cohort, which is an ongoing sing...
Article
Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease (AD) (Jack et al., 2018). Electroencephalography (EEG) is a non‐invasive and cheap technique that would be an interesting screening tool for the preclinical stage of AD. We included participants from the INSIGHT‐preAD cohort, which is an ongoing sing...
Article
Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically...
Preprint
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 remain...
Preprint
Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically...
Article
Full-text available
Objective: Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limit...
Preprint
The extraction of brain functioning features is a crucial step in the definition of brain-computer interfaces (BCIs). In the last decade, functional connectivity (FC) estimators have been increasingly explored based on their ability to capture synchronization between multivariate brain signals. However, the underlying neurophysiological mechanisms...
Article
Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer's disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer's disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer's di...
Preprint
Full-text available
Motor imagery-based brain-computer interfaces (BCIs) use an individuals ability to volitionally modulate localized brain activity as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of...
Preprint
Full-text available
Brain-computer interfaces have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals. Moreover, the neural processes activated by training that enable humans to achieve good control remain poorly understood. In this study, we show that...
Article
In this paper, we present a proof of concept study which demonstrates for the first time the possibility to record magnetocardiography (MCG) signals with 4He vector optically-pumped magnetometers (OPM) operated in a gradiometer mode. Resulting from a compromise between sensitivity, size and operability in a clinical environment, the developed magne...
Thesis
Full-text available
La magnétocardiographie (MCG) et la magnétoencéphalographie (MEG) sont deux techniques d'imagerie non-invasives mesurant respectivement les champs magnétiques cardiaques et cérébraux. Les dispositifs actuels utilisent des capteurs supraconducteurs de haute performance mais nécessitant un dispositif de refroidissement cryogénique, engendrant de fort...
Article
Full-text available
Detecting MCG signals from a phantom with a 4He magnetometer M.C. Corsi (1),, E. Labyt (1), W. Fourcault(1), C. Gobbo(1), F. Bertand(1), F. Alcouffe (1), G Cauffet (2), M. Le Prado (1) , S. Morales (1) (1) CEA, LETI, MINATEC Campus, F-38054 Grenoble, France (2) Univ. Grenoble Alpes, G2Elab, F-38000 Grenoble, France CNRS, G2Elab, F-38000 Grenoble,...
Patent
Full-text available
The invention relates to a magnetic field measurement device, including a detector (4) configured to measure the amplitude of an output signal at a harmonic of an oscillation frequency of an excitation source, said amplitude being proportional to the magnetic field (B) to be measured, characterised in that it comprises an excitation circuit configu...