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Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation

Abstract : Context Passive Brain-Computer Interface (pBCI) has recently gained in popularity through its applications, e.g. workload and attention assessment. Nevertheless, one of the main limitations remains the important intra-and inter-subject variability. We propose a robust approach relying on ensemble learning, grounded in functional connectivity and Riemannian geometry to mitigate the high variability of the data with a large and diverse panel of classifiers.
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https://hal.inria.fr/hal-03359257
Contributor : Marie-Constance Corsi Connect in order to contact the contributor
Submitted on : Thursday, September 30, 2021 - 9:16:00 AM
Last modification on : Friday, September 2, 2022 - 12:36:11 PM
Long-term archiving on: : Friday, December 31, 2021 - 6:17:45 PM

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  • HAL Id : hal-03359257, version 1

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Marie-Constance Corsi, Sylvain Chevallier, Quentin Barthélemy, Isabelle Hoxha, Florian Yger. Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation. Neuroergonomics conference 2021, Sep 2021, Virtual event, Germany. ⟨hal-03359257⟩

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