Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs.
The 8 matches
- [1] § Materials and Methods › Predictive models ↔ LSVR_model.py, lines 37–114 · score 0.92 · temporal weight vector, Gram matrix, dual variables, Longitudinal Support Vector, temporal trends, insensitive
- [2] § Materials and Methods › Predictive models ↔ LSVR_model.py, lines 37–114 · score 0.81 · temporal trend vector, dual coefficients, dual variables, support vectors, QP, linear
- [3] § Materials and Methods › Predictive models ↔ LSVC_model.py, lines 95–176 · score 0.74 · temporal trend vector, dual coefficients, support vectors, QP, linear, iterative
- [4] § Materials and Methods › Predictive models ↔ LSVC_model.py, lines 27–93 · score 0.73 · Gram matrix, Longitudinal Support Vector, temporal trends, QP, dual, model
- [5] § Results › Prediction results › BCI performance prediction ↔ LSVR_model.py, lines 567–648 · score 0.70 · Square Error, LSVR model, continuous predicted, BCI score, RMSE, shuffled
- [6] § Materials and Methods › Predictive models ↔ LSVC_model.py, lines 27–93 · score 0.63 · Longitudinal Support Vector, temporal trend, LSVC, Classifier, model, matrix
- [7] § Materials and Methods › Predictive models ↔ LSVC_model.py, lines 95–176 · score 0.56 · dual coefficients, LSVC, linear, iterative, temporal, matrix
- [8] § Results › Prediction results ↔ LSVC_model.py, lines 528–573 · score 0.53 · Support Vector Classifier, classification model, BCI score, LSVC, longitudinal, predictive
Paper
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The authors' code
Python · 754 lines · 34 KB · no license · 5 matches
LSVC_model.py at commit 45a88f8, no license · at the source
Overview
- Sorbonne Université, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
Abstract
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 key limitation of current protocol: the use of fixed-length sessions training paradigms that ignore individual learning variability. We propose a novel approach based on neuronal avalanches, spatiotemporal cascades of brain activities, as biomarkers to characterize and predict user-specific learning. From electroencephalography data across four sessions in 20 subjects, we characterized avalanches by their length and their spatiotemporal size. These features showed significant training and task effects and were found to correlate to BCI performance across sessions. We further assessed their ability to predict BCI success through longitudinal models, achieving up to 91% accuracy, improved by spatial filtering on selected brain regions. These findings demonstrate the utility of neuronal avalanche dynamics as robust biomarkers for BCI training, supporting the development of personalized protocols aimed at mitigating BCI illiteracy.
Reproduced under the paper's license (CC BY), from the paper cited above.
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CamiMannino/Neuronal-avalanches-as-a-predictive-biomarker-for-guiding-tailored-BCI-training-programs
45a88f8b7911f116d0359b5433c72acd2bd05636, 11 August 2025Availability: 1 check, the latest on 28 September 2026: the link answers
- 28 September 2026: the link answers
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- Activations_across_sessi
ons.py — Python, 2,147 lines, shown from its source - Activations_across_sessi
ons_selected_ROIs.py — Python, 1,594 lines, shown from its source - Hit_Miss_Activations.py — Python, 1,082 lines, shown from its source
- Hit_Miss_Avalanches_leng
th.py — Python, 1,051 lines, shown from its source - LSVC_model.py — Python, 754 lines, 5 matches, shown from its source
- LSVR_model.py — Python, 745 lines, 3 matches, shown from its source
- Mean_Avalanches_Length_a
cross_sessions.py — Python, 2,247 lines, shown from its source - Mean_Avalanches_Length_a
cross_sessions_slected_R — Python, 2,089 lines, shown from its sourceOIs.py - Occurence_ROIs_preATM_we
ighted_hit_miss.py — Python, 1,129 lines, shown from its source - README.md — Text, 6 lines, shown from its source
The paper's code and data availability statement is in the Data section.
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Data
No dataset and no data link were found in the paper.
Data and Code Availability
The study dataset has been fully collected and curated. A dedicated data-descriptor paper is currently in preparation; upon its acceptance, the de-identified dataset and full documentation will be deposited in an open repository with a citable DOI and made publicly available. Until release, controlled access may be granted on reasonable request to the corresponding author.
Code used to import data, analyse data, and generate manuscript figures are available on GitHub: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 4 authors, 9 keywords, 49 references.
Cite
This paper
Mannino, C., Sorrentino, P., Chavez, M., & Corsi, M.-C. (2026). Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1259. https://
BibTeX
@article{mannino2026neur
author = {Mannino, Camilla and Sorrentino, Pierpaolo and Chavez, Mario and Corsi, Marie-Constance},
title = {{Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {4},
pages = {IMAG.a.1259},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/
url = {https://
pmid = {42232076},
pmcid = {PMC13224313}
}
RIS
TY - JOUR
AU - Mannino, Camilla
AU - Sorrentino, Pierpaolo
AU - Chavez, Mario
AU - Corsi, Marie-Constance
TI - Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/
VL - 4
SP - IMAG.a.1259
SN - 2837-6056
PB - MIT Press
DO - 10.1162/
UR - https://
LA - en
ER -
CSL-JSON
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