LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910223117058048 |
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| author | Bdeir, Ahmad Burchert, Johannes Hanika, Tom Schmidt-Thieme, Lars Landwehr, Niels |
| author_facet | Bdeir, Ahmad Burchert, Johannes Hanika, Tom Schmidt-Thieme, Lars Landwehr, Niels |
| contents | Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain-computer interfaces, but remains challenging due to low signal-to-noise ratios and high inter-subject variability. As a result, many existing approaches rely on subject-specific models, which fail to exploit shared structure in neural signals and do not generalize to unseen subjects. To address these limitations, we propose LAtte, a framework that combines Lorentz attention with a hyperbolic InceptionTime-based encoder to improve cross-subject generalization in EEG classification. The model explicitly decomposes EEG signals into a learned baseline component and task-relevant deviations, enabling more structured representation learning. To further improve robustness and adaptability, we incorporate subject-specific low-rank adaptation (LoRA) modules at both encoder and decoder levels, augmented with a Lorentz boost-based LoRA mechanism and hyperbolic projection layers to reduce overfitting in geometric representations. We evaluate LAtte with and without finetuning in three settings: subject-specific, subject-conditional, and leave-one-subject-out (LOSO) on five established EEG datasets, achieving a consistent improvement in performance over current state-of-the-art methods for smaller datasets and maintaining performance for larger datasets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_10881 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification Bdeir, Ahmad Burchert, Johannes Hanika, Tom Schmidt-Thieme, Lars Landwehr, Niels Machine Learning Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain-computer interfaces, but remains challenging due to low signal-to-noise ratios and high inter-subject variability. As a result, many existing approaches rely on subject-specific models, which fail to exploit shared structure in neural signals and do not generalize to unseen subjects. To address these limitations, we propose LAtte, a framework that combines Lorentz attention with a hyperbolic InceptionTime-based encoder to improve cross-subject generalization in EEG classification. The model explicitly decomposes EEG signals into a learned baseline component and task-relevant deviations, enabling more structured representation learning. To further improve robustness and adaptability, we incorporate subject-specific low-rank adaptation (LoRA) modules at both encoder and decoder levels, augmented with a Lorentz boost-based LoRA mechanism and hyperbolic projection layers to reduce overfitting in geometric representations. We evaluate LAtte with and without finetuning in three settings: subject-specific, subject-conditional, and leave-one-subject-out (LOSO) on five established EEG datasets, achieving a consistent improvement in performance over current state-of-the-art methods for smaller datasets and maintaining performance for larger datasets. |
| title | LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.10881 |