# EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks

arXiv: https://arxiv.org/abs/2306.13109 · HTML: https://mlsanity.com/paper/2306.13109/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 26 of 26)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [On the Bottleneck of Graph Neural Networks and its Practical Implications](https://mlsanity.com/paper/2006.05205/) (2020)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Understanding Attention and Generalization in Graph Neural Networks](https://mlsanity.com/paper/1905.02850/) (2019)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Maximum Density Divergence for Domain Adaptation](https://mlsanity.com/paper/2004.12615/) (2020)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1911.11942/) (2019)
- [Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT](https://mlsanity.com/paper/2009.02577/) (2020)
- [Federated Transfer Learning for EEG Signal Classification](https://mlsanity.com/paper/2004.12321/) (2020)
- [EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals](https://mlsanity.com/paper/2106.09135/) (2021)
- [Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface](https://mlsanity.com/paper/1708.06578/) (2017)
- [EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2101.10932/) (2021)
- [HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography](https://mlsanity.com/paper/1909.05784/) (2019)
- [Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding](https://mlsanity.com/paper/2103.05351/) (2021)
- [Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction](https://mlsanity.com/paper/2008.11426/) (2020)
- [Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2112.06654/) (2021)
- [Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI](https://mlsanity.com/paper/1807.11752/) (2018)
- [HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks](https://mlsanity.com/paper/2103.05338/) (2021)

## Built on by (most cited first, 6 of 6)

- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling](https://mlsanity.com/paper/2409.12454/) (2024)
- [Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding](https://mlsanity.com/paper/2507.06779/) (2025)
- [Learning aligned EEG representations with subject-specific encoders](https://mlsanity.com/paper/2606.16462/) (2026)
- [Graph Neural Networks Uncover Geometric Neural Representations in Reinforcement-Based Motor Learning](https://mlsanity.com/paper/2410.23812/) (2024)
- [A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations](https://mlsanity.com/paper/2506.13835/) (2025)

## Abstract (arXiv)

Brain-Machine Interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavailable from a single dataset. Yet, it is difficult to combine data across labs or even data within the same lab collected over the years due to the variation in recording equipment and electrode layouts resulting in shifts in data distribution, changes in data dimensionality, and altered identity of data dimensions. Our objective is to overcome this limitation and learn from many different and diverse datasets across labs with different experimental protocols. To tackle the domain adaptation problem, we developed a novel machine learning framework combining graph neural networks (GNNs) and transfer learning methodologies for non-invasive Motor Imagery (MI) EEG decoding, as an example of BMI. Empirically, we focus on the challenges of learning from EEG data with different electrode layouts and varying numbers of electrodes. We utilise three MI EEG databases collected using very different numbers of EEG sensors (from 22 channels to 64) and layouts (from custom layouts to 10-20). Our model achieved the highest accuracy with lower standard deviations on the testing datasets. This indicates that the GNN-based transfer learning framework can effectively aggregate knowledge from multiple datasets with different electrode layouts, leading to improved generalization in subject-independent MI EEG classification. The findings of this study have important implications for Brain-Computer-Interface (BCI) research, as they highlight a promising method for overcoming the limitations posed by non-unified experimental setups. By enabling the integration of diverse datasets with varying electrode layouts, our proposed approach can help advance the development and application of BMI technologies.