Graph convolutional network-based harmonization of EEG for cross-dataset transfer in motor imagery in BCI

Objective. Electroencephalogram (EEG) electrode configurations vary across Motor Imagery Brain-Computer Interface (MI-BCI) datasets, limiting transfer learning and system performance due to small dataset sizes. This work proposes a spatial harmonization framework that maps heterogeneous EEG recordings to a common physical electrode montage while preserving task-relevant MI information.Approach. Each EEG trial is modelled as a graph, with electrodes as nodes and electrode samples as node embeddings. A two-layer graph convolutional network (GCN) is introduced to capture spatio-temporal relationships between electrodes and EEG samples. The harmonized EEG is evaluated using time-domain, frequency-domain, and spatial-domain analyses, as well as downstream MI classification with EEGNet, FBCNet, and ADFCNN. Performance is assessed under three protocols-within-dataset classification, source-only cross-dataset transfer, and target-domain fine-tuning-across three public MI EEG datasets.Main results. The proposed GCN yields lower harmonization error than spherical spline interpolation while preserving the principal temporal, spectral, and spatial characteristics of MI EEG. In within-dataset classification, combining real and harmonized EEG improved decoding performance, increasing accuracy from 56.57% to 66.20% for EEGNet and from 61.96% to 72.54% for FBCNet on Dataset A. In cross-dataset experiments, the harmonized representation supported both source-only transfer and fine-tuning, with the combined condition generally yielding the highest performance.Significance.The proposed method addresses electrode-layout incompatibility at the EEG signal level without restricting datasets to a small subset of shared electrodes. By enabling heterogeneous MI EEG datasets to be represented in a common physical montage, the framework provides a practical basis for signal-level harmonization, dataset augmentation, and cross-dataset MI decoding across diverse recording setups.