Functional connectivity ensemble method to enhance BCI performance (FUCONE)

TL;DR

FUCONE is a novel ensemble framework that combines multiple functional connectivity estimators and covariance-based pipelines to significantly improve brain-computer interface classification accuracy across various datasets.

Contribution

The paper introduces FUCONE, a new ensemble method leveraging diverse functional connectivity estimators and Riemannian classifiers for enhanced BCI performance.

Findings

FUCONE outperforms state-of-the-art methods across datasets.

Ensemble diversity improves robustness to variability.

Meta-analysis confirms significant performance gains.

Abstract

Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and that is mostly applied for brain activity analysis. Building on the advances in information geometry for brain-computer interface, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to classify mental states, such as motor imagery. A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline, called FUCONE, is evaluated on different conditions and datasets. Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods. The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability.