论文标题
空间图信号插值与将BCI数据集合并为各种维度的应用
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
论文作者
论文摘要
BCI运动图像数据集通常很小，并且具有不同的电极设置。在训练深层神经网络时，人们可能希望利用所有这些数据集，以增加可用的数据量，从而获得良好的概括结果。为此，我们引入了一种空间图信号插值技术，该技术允许有效地插入多个电极。我们使用五个BCI运动图像数据集进行了一组实验，将提出的插值与球形花键插值进行了比较。我们认为，这项工作提供了有关如何利用图表插值电极以及如何匀浆多个数据集的新颖想法。
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.
