Ensemble of Riemannian classifiers for multimodal data: FUCONE approach for M/EEG data
Résumé
Controlling a brain-computer interface (BCI) requires time to achieve high performance. Despite its clinical applications, one of the main drawbacks is the high inter-subject variability that could be noticed for performance. This is sometimes referred in the literature as the « BCI inefficiency » phenomenon and affects its usability. Among the approaches adopted to tackle these issues are the search for neuromarkers that potentially capture better the neurophysiological mechanisms underlying the BCI performance and the optimization of the classification pipelines, that could be robust enough to be applied to any subject. In this work, we proposed an original approach that combines functional connectivity estimators, Riemannian geometry and ensemble learning to ensure a robust classification.
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