  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.
