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

arXiv: https://arxiv.org/abs/2211.02624 · HTML: https://mlsanity.com/paper/2211.02624/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [MOABB: Trustworthy algorithm benchmarking for BCIs](https://mlsanity.com/paper/1805.06427/) (2018)
- [Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach](https://mlsanity.com/paper/1808.05464/) (2018)
- [2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets](https://mlsanity.com/paper/2202.12950/) (2022)
- [Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction](https://mlsanity.com/paper/1703.01842/) (2017)
- [Exploiting Multiple EEG Data Domains with Adversarial Learning](https://mlsanity.com/paper/2204.07777/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding](https://mlsanity.com/paper/2401.10746/) (2024)
- [ESTformer: Transformer utilising spatiotemporal dependencies for electroencephalogram super-resolution](https://mlsanity.com/paper/2312.10052/) (2023)
- [Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding](https://mlsanity.com/paper/2403.15438/) (2024)

## Abstract (arXiv)

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.