Hyperbolic aggregation improves eeg brain region representation learning
HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning
Machine Learning
Summary
Electroencephalography (EEG) recordings vary a lot between people and setups, making it hard for computers to learn from them reliably. The authors developed HyBRA, a new technique that groups EEG signals from different brain regions using hyperbolic geometry and extra information about where electrodes are placed. This approach creates stable signal summaries that work well across multiple EEG datasets. Tests show HyBRA is more accurate and efficient than previous methods.
What this means in practice
- •For brain computer interface developers: Improve EEG signal interpretation across devices by encoding brain region data with hyperbolic geometry for more consistent user control signals.
- •For medical device engineers: Enhance robustness of EEG-based diagnostic tools by unifying heterogeneous electrode layouts into a common representation.
Authors
Xin Liu, Feng Lu, Wei Li
Abstract
Electroencephalography (EEG) representation learning is challenged by noisy signals, inter-subject variability, and heterogeneous electrode montages in different datasets and recording settings. Recent self-supervised EEG pretraining methods improve transferability by converting heterogeneous recordings into unified token or sequence representations, but channel-aware designs may still encode montage- or dataset-specific regularities. We propose HyBRA, a self-supervised framework based on hyperbolic global-local brain-region aggregation. HyBRA maps heterogeneous channel observations into a fixed regional token space by combining electrode coordinates, region priors, and feature-dependent hyperbolic distances. A channel-evidence injection branch preserves fine-grained discriminative cues during aggregation. The resulting tokens are processed by a Region-Time Transformer and pretrained with masked reconstruction of latent features and region-level signals. HyBRA outperforms the compared methods in average balanced accuracy on six benchmark datasets. Under a controlled six-fold evaluation on OpenBMI-MI, HyBRA matches CSBrain in balanced accuracy, improves AUROC by 1.84 percentage points and AUPRC by 5.23 percentage points, and uses 96.8\% fewer parameters. Matched comparisons on TUAB and TUEV show higher balanced accuracy for hyperbolic matching than for Euclidean distance or cosine similarity.