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Conference Papers Year : 2023

Ensemble of Riemannian Classifiers for Multimodal Data: FUCONE Approach for M/EEG Data

Abstract

Interfacing computers directly with brain signals is a fascinating but difficult task, due to subject variability. Methods based on Riemannian geometry have improved the state of the art for Brain-Computer Interface (BCI), capturing some invariant signature in electroencephalography (EEG) signals. So far, simple features have been used and alternative ones, reflecting interconnected nature of brain activity have been overlooked. Here, we evaluated a framework, called FUCONE, that consists in combining functional connectivity estimators, Riemannian geometry, and ensemble learning, and we assessed its relevance for fusion from magnetoencephalography (MEG) and EEG signals.
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hal-04140126 , version 1 (24-06-2023)

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  • HAL Id : hal-04140126 , version 1

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Marie-Constance Corsi, Sylvain Chevallier, Fabrizio de Vico Fallani, Florian Yger. Ensemble of Riemannian Classifiers for Multimodal Data: FUCONE Approach for M/EEG Data. ISBI 2023 - IEEE International Symposium on Biomedical Imaging, Apr 2023, Cartagena de Indias, Colombia. ⟨hal-04140126⟩
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