LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification
This addresses the problem of high inter-subject variability in EEG classification for applications like medical diagnostics and brain-computer interfaces, representing a novel approach to cross-subject training.
The paper tackled cross-subject EEG classification by proposing LAtte, a framework integrating Lorentz Attention with an InceptionTime-based encoder, which achieved substantial performance improvements over state-of-the-art methods on three datasets.
Electroencephalogram (EEG) classification is critical for applications ranging from medical diagnostics to brain-computer interfaces, yet it remains challenging due to the inherently low signal-to-noise ratio (SNR) and high inter-subject variability. To address these issues, we propose LAtte, a novel framework that integrates a Lorentz Attention Module with an InceptionTime-based encoder to enable robust and generalizable EEG classification. Unlike prior work, which evaluates primarily on single-subject performance, LAtte focuses on cross-subject training. First, we learn a shared baseline signal across all subjects using pretraining tasks to capture common underlying patterns. Then, we utilize novel Lorentz low-rank adapters to learn subject-specific embeddings that model individual differences. This allows us to learn a shared model that performs robustly across subjects, and can be subsequently finetuned for individual subjects or used to generalize to unseen subjects. We evaluate LAtte on three well-established EEG datasets, achieving a substantial improvement in performance over current state-of-the-art methods.