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...

Full description

Saved in:
Bibliographic Details
Main Authors Ouahidi, Yassine El, Drumetz, Lucas, Lioi, Giulia, Farrugia, Nicolas, Pasdeloup, Bastien, Gripon, Vincent
Format Journal Article
LanguageEnglish
Published 28.10.2022
Subjects
Computer Science - Learning
Online AccessGet full text

Cover

More Information
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.
DOI:10.48550/arxiv.2211.02624