LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification

• URL: http://arxiv.org/abs/2603.10881v1
• Date: Wed, 11 Mar 2026 15:27:39 GMT
• Title: LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification
• Abstract summary: LAtte is a novel framework that integrates a Lorentz Attention Module with an InceptionTime-based encoder to enable robust and generalizable EEG classification.<n>First, we learn a shared baseline signal across all subjects using pretraining tasks to capture common underlying patterns.<n>Then, we utilize novel Lorentz low-rank adapters to learn subject-specific embeddings that model individual differences.<n>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.
• Score: 13.887781479252332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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