Interpolation spatiale de signaux sur graphes avec une application pour la fusion d'ensembles de données EEG de diverses dimensions
Résumé
BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase the amount of data available and hence obtain good generalization results. To this end, we introduce a spatial graph signal interpolation technique, that allows to interpolate efficiently multiple electrodes. We conduct a set of experiments with five BCI Motor Imagery datasets comparing the proposed interpolation with spherical splines interpolation. We believe that this work provides novel ideas on how to leverage graphs to interpolate electrodes and on how to homogenize multiple datasets.
Domaines
Informatique [cs]
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Ouahidi et al. - Interpolation spatiale de signaux sur graphes avec.pdf (661.28 Ko)
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