Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation
Résumé
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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BCCHY-NEC21-abstract.pdf (682.81 Ko)
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BCCHY-NEC21-poster.pdf (575.12 Ko)
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