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

Corsi, Marie-Constance; Chevallier, Sylvain; Barthélemy, Quentin; Hoxha, Isabelle; Yger, Florian, Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation, Neuroergonomics conference 2021, 2021-09

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Type
Communication / Conférence
Conference title
Neuroergonomics conference 2021
Conference date
2021-09
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Author(s)
Corsi, Marie-Constance
Chevallier, Sylvain
Barthélemy, Quentin
Hoxha, Isabelle
Yger, Florian
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
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.
Subjects / Keywords
Riemannian geometry; Functional connectivity; Ensemble learning