Title: Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer Show Chinese title

Title: 跨模态癫痫信号和谐化：频域映射量化用于统一神经生理学Transformer的预训练

Abstract: Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are vital for epilepsy diagnosis and treatment. Their unified analysis offers the potential to harness the complementary strengths of each modality but is challenging due to variations in recording montages, amplitude and signal-to-noise ratio (SNR), and frequency components. To address the aforementioned challenges, this paper introduces EpiNT, a novel Transformer-based pre-trained model for unified EEG and iEEG analysis. EpiNT employs channel-independent modeling with masked autoencoders (MAE) and vector quantization (VQ), along with a frequency domain mapping quantizer to capture crucial frequency features. Pre-trained on over 2,700 hours of multi-modal clinical neurophysiological data from 1,199 patients, EpiNT outperformed both randomly initialized models and other pre-trained methods on six downstream classification tasks, demonstrating robust representation learning capabilities. This work presents a promising approach for unified epilepsy neurophysiology analysis. Show Chinese abstract

Abstract: 头皮脑电图（EEG）和颅内脑电图（iEEG）对于癫痫的诊断和治疗至关重要。它们的统一分析可以利用每种模态的互补优势，但由于记录 montage、振幅、信噪比（SNR）以及频率成分的变化，这一分析极具挑战性。为了解决上述挑战，本文介绍了一种名为 EpiNT 的新型基于 Transformer 的预训练模型，用于统一的 EEG 和 iEEG 分析。EpiNT 使用通道无关建模，结合掩码自编码器（MAE）和向量量化（VQ），以及频域映射量化器来捕获关键频率特征。在来自 1,199 名患者的超过 2,700 小时多模态临床神经生理数据上预训练后，EpiNT 在六个下游分类任务中优于随机初始化模型和其他预训练方法，展示了强大的表示学习能力。这项工作为统一的癫痫神经生理学分析提供了一种有前景的方法。