用低成本脑电数据生成高精度脑部影像，提升神经成像效率
From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis
- 基于编码器-解码器结构，跨模态融合多尺度脑电信号特征
- 在三个公开数据集上SSIM指标优于现有方法，达当前最佳水平
- 适合神经科学、临床诊断等领域，降低高端脑成像使用门槛
功能性磁共振成像（fMRI）虽能提供大脑活动的宝贵信息，但受限于高昂的运营成本和巨大的基础设施需求。相比之下，脑电图（EEG）可实现毫秒级时间分辨率，却缺乏精确定位神经活动的空间精度。为弥合这一差距，本文提出E2fNet——一种简单而高效的深度学习模型，用于从低成本的EEG数据中合成fMRI图像。E2fNet是一种专为捕捉并转换来自不同电极通道的多尺度有意义特征而设计的编码器-解码器网络，从而生成准确的fMRI表示。在三个公开数据集上的广泛评估表明，E2fNet始终优于现有的基于CNN和Transformer的方法，在结构相似性指数（SSIM）方面达到最新最优水平。结果表明，E2fNet是一种有前景的、成本效益高的神经成像增强方案。代码已开源：https://github.com/kgr20/E2fNet。
原文摘要 · Abstract (English)
While functional magnetic resonance imaging (fMRI) offers valuable insights into brain activity, it is limited by high operational costs and significant infrastructural demands. In contrast, electroencephalography (EEG) provides millisecond-level precision in capturing electrical activity but lacks the spatial fidelity necessary for precise neural localization. To bridge these gaps, we propose E2fNet, a simple yet effective deep learning model for synthesizing fMRI images from low-cost EEG data. E2fNet is an encoder-decoder network specifically designed to capture and translate meaningful multi-scale features from EEG across electrode channels into accurate fMRI representations. Extensive evaluations across three public datasets demonstrate that E2fNet consistently outperforms existing CNN- and transformer-based methods, achieving state-of-the-art results in terms of the structural similarity index measure (SSIM). These results demonstrate that E2fNet is a promising, cost-effective solution for enhancing neuroimaging capabilities. The code is available at https://github.com/kgr20/E2fNet.
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