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Communication Dans Un Congrès Année : 2023

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

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.

Dates et versions

hal-04173113 , version 1 (28-07-2023)

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Citer

Yassine El Ouahidi, Lucas Drumetz, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, et al.. Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities. ICASSP 2023: IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2023, Rhodes Island, Greece. pp.1-5, ⟨10.1109/ICASSP49357.2023.10097201⟩. ⟨hal-04173113⟩
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