Marie-Constance Corsi

Marie-Constance Corsi

Postdoctoral researcher

Paris Brain Institute

Biography

Marie-Constance Corsi is a Postdoctoral researcher at Paris Brain Institute in the ARAMIS Lab. Her 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. She essentially considers 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.

She serves as secretary general of the French academic association promoting the advances in BCI, called CORTICO, and as active member of the Student and Postdoc Committee of the BCI Society.

You can download my CV in pdf

Interests

  • Closed-loop systems (Brain-Computer Interfaces & Neurofeedback)
  • Functional connectivity
  • Multimodal integration
  • Biomedical instrumentation

Education

  • 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)

News

Talk on M/EEG data analysis during the next Cortico days !

Glad to announce that I will give a talk during the day dedicated to early-career researchers (JJC-ICON’2021) of the Cortico days 2021. I will present an overview of the M/EEG signals and the associated data analysis. The video and the materials of my talk are available here !

Abstract accepted for oral presentation in Networks 2021 conference !

Pleased to announce that I will give a presentation during the Networks 2021 conference. I will present my last piece of work on the use of multilayer appproaches to elicit patterns of BCI learning. More info to come !

Projects

Neurophysiological markers of longitudinal processes

Functional connectivity and brain network reorganization underlying longitudinal processes, mainly BCI training

Improving neural decoders

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

Helium 4 optically-pumped magnetometers

Development of cryogenic-free sensors for magnetocardiography and magnetoencephalography

Recent Publications

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A machine learning approach to screen for preclinical Alzheimer's disease

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer’s disease (AD). We included 304 cognitively normal …

BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks

Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop …
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks

Functional disconnection of associative cortical areas predicts performance during BCI training

Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. …
Functional disconnection of associative cortical areas predicts performance during BCI training

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