Neuronal avalanches as a predictive biomarker of BCI performance: towards a tool to guide tailored training program

TL;DR

This study introduces neuronal avalanches as biomarkers to predict and enhance BCI training success, enabling personalized protocols to address BCI inefficiency.

Contribution

It proposes a novel EEG-based approach using neuronal avalanche features to predict BCI performance and guide tailored training strategies.

Findings

Significant correlation between avalanche features and BCI performance

Predictive models achieved up to 91% accuracy in forecasting BCI success

Spatial filtering improved prediction accuracy

Abstract

Brain-Computer Interfaces (BCIs) based on motor imagery (MI) hold promise for restoring control in individuals with motor impairments. However, up to 30% of users remain unable to effectively use BCIs-a phenomenon termed ''BCI inefficiency.'' This study addresses a major limitation in current BCI training protocols: the use of fixed-length training paradigms that ignore individual learning variability. We propose a novel approach that leverages neuronal avalanches-spatiotemporal cascades of brain activity-as biomarkers to characterize and predict user-specific learning mechanism. Using electroencephalography (EEG) data collected across four MI-BCI training sessions in 20 healthy participants, we extracted two features: avalanche length and activations. These features revealed significant training and taskcondition effects, particularly in later sessions. Crucially, changes in these features across sessions ($\Delta$avalanche length and $\Delta$activations) correlated significantly with BCI performance and enabled prediction of future BCI success via longitudinal Support Vector Regression and Classification models. Predictive accuracy reached up to 91%, with notable improvements after spatial filtering based on selected regions of interest. 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.