MEL：用于fMRI转换的坐标保留型EEG分词

中文摘要 AI 辅助

将脑电图（EEG）转换为功能磁共振成像（fMRI）对医学神经成像、临床脑状态监测和多模态神经解码具有重要意义，因为该任务旨在从快速且易获取的电生理记录中推断出空间组织的血流动力学活动。现有EEG到fMRI的研究主要追求更强的解码器，但该问题还受限于表示接口不匹配：fMRI响应存在延迟、时间整合且空间分布，而通用EEG编码常混淆时间延迟、通道身份和频带结构。我们提出多频段EEG隐状态分词（MEL），这是一种坐标保留型EEG表示框架，它将每个目标fMRI响应锚定到其先前的EEG历史，并将其组织为延迟-通道-频率神经状态分词。通过显式捕获血流动力学延迟和频谱-空间动态，MEL使与fMRI相关的EEG表示与容量可控的读出对齐，无需完全依赖模型缩放。在VU EEG-fMRI基准数据集和外部Oddball数据上的实验表明，MEL相比强大的NeuroBOLT基线提升了预测性能。消融实验和对照实验进一步表明，该提升来自结构化EEG表示，而非数据泄露、捷径统计或解码器容量。

英文摘要

Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state monitoring, and multimodal neural decoding, because it aims to infer spatially organized hemodynamic activity from fast and accessible electrophysiological recordings. Existing EEG-to-fMRI studies mainly pursue stronger decoders, but the problem is also constrained by a representation-interface mismatch: fMRI responses are delayed, temporally integrated, and spatially distributed, whereas generic EEG encodings often entangle temporal lag, channel identity, and frequency-band structure. We propose Multi-band EEG Latent-state Tokenization (MEL), a coordinate-preserving EEG representation framework that anchors each target fMRI response to its preceding EEG history and organizes it into lag-channel-frequency neural-state tokens. By explicitly capturing hemodynamic latency and spectral-spatial dynamics, MEL aligns fMRI-pertinent EEG representations with capacity-controlled readouts without depending entirely on model scaling. Experiments on VU EEG-fMRI benchmarks and external Oddball data show that MEL improves prediction over strong NeuroBOLT baselines. Ablations and controls further indicate that the gains come from structured EEG representation rather than leakage, shortcut statistics, or decoder capacity.

发表机构

• Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）
• The University of Hong Kong（香港大学）
• Peking University（北京大学）

机构由 AI 辅助整理，请以论文原文为准。