Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities

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 si...

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Bibliographic Details
Published inarXiv.org
Main Authors Yassine El Ouahidi, Lucas Drumetz, Lioi, Giulia, Farrugia, Nicolas, Bastien Pasdeloup, Gripon, Vincent
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.10.2022
Subjects
Artificial neural networks
Datasets
Electrodes
Imagery
Interpolation
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Summary: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.
ISSN:2331-8422