Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 4, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

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

Camilla Mannino¹, Pierpaolo Sorrentino^2,3, Mario Chavez¹

¹Sorbonne Université, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France.

Imaging Neuroscience (Cambridge, Mass.)

|June 3, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cyclic Recurrence of Seizures as a Marker for Super-Refractory Status Epilepticus.

Brain and behavior·2026

Same author

The neurotoxic legacy of CAR-T cells: where do we stand?

Therapeutic advances in neurological disorders·2026

Same author

Neurophysiological screening of individual variability for robust decoding in c-VEP-based BCI.

Imaging neuroscience (Cambridge, Mass.)·2026

Same author

Opioid-associated modulation of respiratory-related cortical activity in dyspnoeic mechanically ventilated patients: An electroencephalographic study.

Annals of intensive care·2026

Same author

Exploring pathways leading to drug-resistant epilepsy for patients with cryptogenic new onset refractory status epilepticus.

Epilepsia·2026

Same author

Imagined movement modulates cardiac-cortico-cortical and cardiac-cortico-cerebellar oscillatory networks.

NeuroImage·2026

This summary is machine-generated.

Brain-Computer Interfaces (BCIs) can be challenging for some users. This study uses neuronal avalanches to predict BCI success, potentially leading to personalized training protocols and improved outcomes for individuals with motor impairments.

Area of Science:

Neuroscience
Biomedical Engineering
Computational Neuroscience

Background:

Motor imagery-based Brain-Computer Interfaces (BCIs) offer functional restoration for individuals with motor impairments.
A significant portion of users (up to 30%) experience 'BCI inefficiency,' struggling to effectively utilize these systems.
Current BCI training protocols often employ fixed session lengths, failing to account for individual learning variability.

Purpose of the Study:

To introduce a novel approach utilizing neuronal avalanches as biomarkers for characterizing and predicting user-specific learning in BCI training.
To address the limitations of fixed-length training paradigms by incorporating individual learning dynamics.
To develop personalized BCI protocols to mitigate BCI illiteracy.

Main Methods:

Keywords:

BCI-score repeated correlation Brain-Computer Interface (BCI)Electroencephalography (EEG)learning effect longitudinal predictive model motor imagery neuronal avalanches personalized training protocol task-condition effect

Related Experiment Videos

Last Updated: Jun 4, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Analysis of electroencephalography (EEG) data from 20 subjects across four training sessions.
Characterization of neuronal avalanches by their spatiotemporal length and size.
Application of longitudinal models and spatial filtering to assess predictive capabilities.

Main Results:

Neuronal avalanche features demonstrated significant effects related to training and task performance.
Avalanche characteristics correlated with BCI performance across multiple sessions.
A predictive model based on neuronal avalanche dynamics achieved up to 91% accuracy in forecasting BCI success.

Conclusions:

Neuronal avalanche dynamics serve as robust biomarkers for optimizing BCI training.
The findings support the development of adaptive, personalized BCI protocols.
This approach has the potential to significantly reduce BCI illiteracy and enhance user control.