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.

翻译：脑电图（EEG）分类对于从医疗诊断到脑机接口的广泛应用至关重要，但由于其固有的低信噪比（SNR）和高被试间变异性，该任务仍然具有挑战性。为解决这些问题，我们提出了LAtte，一种新颖的框架，它将洛伦兹注意力模块与基于InceptionTime的编码器相结合，以实现鲁棒且可泛化的EEG分类。与先前主要评估单被试性能的工作不同，LAtte专注于跨被试训练。首先，我们通过预训练任务学习所有被试共享的基线信号，以捕捉共同的潜在模式。然后，我们利用新颖的洛伦兹低秩适配器来学习建模个体差异的被试特定嵌入。这使得我们能够学习一个在被试间表现鲁棒的共享模型，该模型随后可以针对个体被试进行微调，或用于泛化到未见过的被试。我们在三个公认的EEG数据集上评估了LAtte，其性能相较于当前最先进的方法取得了显著提升。