Corsi MC, Chevallier S, Fallani FDV, Yger F. Functional connectivity ensemble method to enhance BCI performance (FUCONE).
IEEE Trans Biomed Eng 2022;
69:2826-2838. [PMID:
35226599 DOI:
10.1109/tbme.2022.3154885]
[Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
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.
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