• DOI:10.1109/TBME.2022.3154885
• Corpus ID: 243832905

Functional connectivity ensemble method to enhance BCI performance (FUCONE)

@article{Corsi2022FunctionalCE,
  title={Functional connectivity ensemble method to enhance BCI performance (FUCONE)},
  author={Marie-Constance Corsi and Sylvain Chevallier and Fabrizio de Vico Fallani and Florian Yger},
  journal={IEEE transactions on bio-medical engineering},
  year={2022},
  volume={PP}
}

• M. Corsi, S. Chevallier, +1 author F. Yger
• Published 4 November 2021
• Computer Science
• IEEE transactions on bio-medical engineering

OBJECTIVE
Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery.

METHODS
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 among those tested, called FUCONE, is evaluated on different conditions and datasets.

RESULTS
Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods.

CONCLUSION
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.

SIGNIFICANCE
Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.