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Marie-Constance Corsi

@mconstancecorsi.bsky.social

310 followers 290 following 33 posts

Research scientist @nerv-lab.bsky.social @institutducerveau.bsky.social @inria_paris @Inserm @CNRS Brain-Computer Interfaces, Functional connectivity, Medical instrumentation Webpage: marieconstance-corsi.netlify.app

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Marie-Constance Corsi @mconstancecorsi.bsky.social · May 30

Launch of NeurIPS 2025 EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding! 🚀

* 3,000+ subjects, 128-channel EEG, 6 cognitive tasks.
* Challenge 1: Generalize to new tasks and individuals.
* Challenge 2: Predict latent psychopathology factors using EEG data.

👉 eeg2025.github.io

EEG Challenge (2025)

From Cross-Task to learning subject invariance representation in EEG decoding

eeg2025.github.io

Marie-Constance Corsi @mconstancecorsi.bsky.social · Sep 5

🔗 ohbm-environment.org/ohbm-satelli...

OHBM Satellite Event – OHBM SEA-SIG

ohbm-environment.org

Marie-Constance Corsi @mconstancecorsi.bsky.social · Sep 5

🧠 Join us for the Brain Models for Multimodal Integration Symposium!
📅 12 September 2025 | ⏰ 12:00–13:15 UTC | 🌐 Virtual Event

Thrilled to co-chair w/ @pierpasorre.bsky.social at the @ohbmofficial.bsky.social Satellite Meeting! Feat. D. Depannemaecker & @gianmarcoduma.bsky.social ma.bsky.social.

https://ohbm-environment.org/ohbm-satellite-event/

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Marie-Constance Corsi @mconstancecorsi.bsky.social · Sep 4

This approach opens doors to adaptive, individualized BCI training protocols informed by model-tracked neural changes.

Huge thanks to the FACE Foundation, our teams and institutions for their support: IIT Madras, @nerv-lab.bsky.social @institutducerveau.bsky.social

Marie-Constance Corsi @mconstancecorsi.bsky.social · Sep 4

These parameter shifts are robust across EEG and MEG, and localize to sensorimotor regions—crucial hubs for motor imagery in BCI.

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Marie-Constance Corsi @mconstancecorsi.bsky.social · Sep 4

We introduce mi-NMM: a linear neural mass model that nails power spectral density shapes—both at rest and during motor imagery tasks.
It tracks intra-regional connectivity strength and E/I population dynamics, revealing how training reshapes neural activity.

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Marie-Constance Corsi @mconstancecorsi.bsky.social · Sep 4

🚨 Preprint alert 🚨

We used a linear neural mass model to reveal regional E/I changes during motor imagery BCI training!

Great study led by @apurbadebnath.bsky.social, w/ @tristanvenot.bsky.social & @parulv1.bsky.social

www.biorxiv.org/content/10.1... 🧵

Neural mechanisms of training in Brain-Computer Interface: A Biophysical modeling approach

Brain-computer interface (BCI) is a system that translates neural activity into commands, allowing direct communication between the brain and external devices. Despite its clinical application, BCI sy...

www.biorxiv.org

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Marie-Constance Corsi @mconstancecorsi.bsky.social · Sep 4

These parameter shifts are robust across EEG and MEG, and localize to sensorimotor regions—crucial hubs for motor imagery in BCI.

Marie-Constance Corsi @mconstancecorsi.bsky.social · Sep 4

We introduce a linear neural mass modeling approach, mi-NMM, that accurately captures power spectral density shapes in resting state & while performing a motor imagery task. It tracks intra-regional connectivity strength and E/I population dynamics, revealing how training reshapes neural activity.

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Reposted by Marie-Constance Corsi

Luc Estebanez @lucestebanez.bsky.social · Sep 4

1/4 Today we introduce the first upper limb prosthesis for the mouse model, controlled by a brain-machine interface! We show that mice can control this prosthesis via a brain-machine interface to solve a rewarded task.

Left: the upper-limb prosthesis can collect water from a tank, and bring it close to the mouth. Right: skeleton of the prosthesis with 4 DOF. THis prosthesis relies on Bowden cables.

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Reposted by Marie-Constance Corsi

CuttingEEG @cuttingeeg.bsky.social · Aug 28

⏳Only 2 months to go until #PracticalMEEG2025!
🎓Come & learn essentials from MNE•FieldTrip•Brainstorm•EEGLAB
🧠We've a few spots left for Brainstorm trainEErs. We're offering 2 early-bird fees before Sept 12 for those interested in becoming BST ambassadors!
Sign up now👉 cuttingeeg.org/practicalmee...

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Reposted by Marie-Constance Corsi

The Organization for Human Brain Mapping @ohbmofficial.bsky.social · Aug 28

🗓️ Only ONE WEEK to register for the OHBM Virtual Satellite Meeting, hosted with the SEA-SIG @ohbmenvironment.bsky.social! There will be an educational course, 3 symposia, and a can’t-miss keynote from Dr. Michael Breakspear! You won’t want to miss it!

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Reposted by Marie-Constance Corsi

Jordy Thielen @jordy-thielen.bsky.social · Aug 28

🧠 Recent article in BPEX
This study shows BCI inefficiency in c-VEP BCI, neurophysiological predictors of that inefficiency, and how personalisation can boost performance and comfort. A step toward high-speed, accessible, user-friendly BCI!
📖 doi.org/10.1088/2057...

#BCI #Neurotechnology #EEG

Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort - IOPscience

Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort, Thielen, Jordy

doi.org

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Marie-Constance Corsi @mconstancecorsi.bsky.social · Aug 27

Key findings:
✅ Neuronal avalanches are robust biomarkers of individual BCI learning.
✅ Features like avalanche duration and spatiotemporal size correlate with BCI performance across sessions.
✅ Longitudinal models using these features achieve up to 91% accuracy in predicting BCI success.

Marie-Constance Corsi @mconstancecorsi.bsky.social · Aug 27

Why it matters?
Up to 30% of users struggle with motor imagery-based BCI, a challenge known as "BCI inefficiency." Current protocols use fixed-length sessions, ignoring individual variability. Our study introduces a novel approach that rely on neuronal avalanches to tackle this issue.

Marie-Constance Corsi @mconstancecorsi.bsky.social · Aug 27

📢 Preprint alert! 🧠💡

Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs

🔗 www.biorxiv.org/content/10.1...

A great work led by Camilla Mannino w/ @pierpasorre.bsky.social & @neurodynamicslab.bsky.social! @nerv-lab.bsky.social @institutducerveau.bsky.social 🧵

Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs

Motor imagery-based Brain-Computer Interfaces (BCIs) restore control in persons with motor impairments, but up to 30% of users struggle—a phenomenon known as “BCI inefficiency”. This study tackles a k...

www.biorxiv.org

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Reposted by Marie-Constance Corsi

The Organization for Human Brain Mapping @ohbmofficial.bsky.social · Aug 14

Have you wanted to use machine learning in your neuroimaging workflow but aren’t sure where to start? 🧠 Join our Educational Course “Navigating machine learning pitfalls and explainability in brain-behavioral predictive modeling” on Sept 10, 12–16 UTC!
🔗https://humanbrainmapping.org/25SEASIG

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Reposted by Marie-Constance Corsi

Decision, Action, and Neural Computation (DANC) lab @danclab.bsky.social · Jul 23

Beta burst waveforms in PD! We find that specific types of beta bursts are rate-modulated by l-dopa, and STN-cortical connectivity during these burst types is related to clinical improvement. Fantastic work by Hasnae Agouram with @pierpasorre.bsky.social 🧠📈

Maciek Szul @maciekszul.bsky.social · Jul 16

🚨
Levodopa increased burst waveform-specific connectivity between STN and the sensorimotor cortex, which correlated with medication related improvement.

Beta burst waveforms look like a promising biomarker candidate for both disease severity and treatment response.

www.medrxiv.org/content/10.1...

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Reposted by Marie-Constance Corsi

camille-mazzara.bsky.social @camille-mazzara.bsky.social · Jul 23

🧠 New Preprint out!
Brain fluidity: a functional marker of neuronal death in Alzheimer’s disease (AD).
Our results highlight the link between dynamic brain connectivity disruptions and neurodegeneration in AD.
@pierpasorre.bsky.social @gianmarcoduma.bsky.social www.medrxiv.org/content/10.1...

Brain Fluidity as a Functional Marker of Tau-Related Neurodegeneration in Alzheimer’s Disease

Alzheimer’s Disease (AD) is the most common form of dementia and one of the leading neurodegenerative disorders worldwide. Neurophysiopathologically, AD is characterized by neuronal death, accompanied...

www.medrxiv.org

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Reposted by Marie-Constance Corsi

CuttingEEG @cuttingeeg.bsky.social · Jun 27

🎉 Excited to share our new article in PLOS Sustainability and Transformation!
Dive into CuttingGardens 2023 and let’s rethink how we gather and care for our communities, together. 🌱🌍
Next edition coming in 2026—stay tuned! 💐
journals.plos.org/sustainabili...

Doing conferences differently: A decentralised multi-hub approach for ecological and social sustainability

Author summary Conferences are very important for career progression but at the same time they create negative ecological and social impact. For example, flying to a traditional in-person conference c...

journals.plos.org

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Reposted by Marie-Constance Corsi

Arthur Desbois @arthurdesbois.bsky.social · Jun 23

Earlier this month, I had the pleasure of attenting #BCI Meeting 2025 @bcisociety.bsky.social in Banff 🍁🧠

I contributed to a workshop by presenting the HappyFeat software, aiming to facilitate the extraction and selection of classification features for BCI pipelines.

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Marie-Constance Corsi @mconstancecorsi.bsky.social · Jun 8

Main features of the workshop are available through the dedicated github page (that will be updated with some surprises soon!): github.com/mccorsi/BCI-...

GitHub - mccorsi/BCI-2025-Features

Contribute to mccorsi/BCI-2025-Features development by creating an account on GitHub.

github.com

Marie-Constance Corsi @mconstancecorsi.bsky.social · Jun 8

José del R. Millán from the University of Texas presented his latest findings on transfer learning based on Riemannian geometry and transcutaneous electrical spinal stimulation to foster BCI learning.

Marie-Constance Corsi @mconstancecorsi.bsky.social · Jun 8

Reinhold Scherer from the University of Essex provided an overview of co-adaptation methods to address feature variability

Marie-Constance Corsi @mconstancecorsi.bsky.social · Jun 8

Sonja Kleih-Dahms and @settgast.bsky.social from Würzburg University, discussed the use of brain criticality to predict BCI performance in Amyotrophic Lateral Sclerosis

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Marie-Constance Corsi @mconstancecorsi.bsky.social · Jun 8

Arthur Desbois from @nerv-lab.bsky.social gave a showcase of HappyFeat, a software designed to guide experimenters during the feature selection process

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