Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
This work addresses the challenge of small and heterogeneous BCI datasets for researchers and practitioners, enabling better generalization in deep neural network training, though it is incremental as it builds on existing interpolation methods.
The paper tackles the problem of merging Brain-Computer Interface (BCI) Motor Imagery datasets with varying electrode setups by introducing a spatial graph signal interpolation technique, achieving efficient interpolation of multiple electrodes compared to spherical splines interpolation in experiments across five datasets.
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.