Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Han:2023:1741-2552/ad09ff,
author = {Han, J and Wei, X and Faisal, AA},
doi = {1741-2552/ad09ff},
journal = {Journal of Neural Engineering},
title = {EEG decoding for datasets with heterogenous electrode configurations using transfer learning graph neural networks},
url = {http://dx.doi.org/10.1088/1741-2552/ad09ff},
volume = {20},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Objective. 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. Approach. 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 utilize 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). Main results. 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. Significance. The findings of this study have important implications for brain-computer-interface 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.
AU  - Han,J
AU  - Wei,X
AU  - Faisal,AA
DO  - 1741-2552/ad09ff
PY  - 2023///
SN  - 1741-2552
TI  - EEG decoding for datasets with heterogenous electrode configurations using transfer learning graph neural networks
T2  - Journal of Neural Engineering
UR  - http://dx.doi.org/10.1088/1741-2552/ad09ff
UR  - http://hdl.handle.net/10044/1/108047
VL  - 20
ER  -
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