English(EN) Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

综述文章回顾了用于跨主题脑电图解码挑战的深度学习方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-01 04:00

本综述文章回顾了旨在提高脑电图（EEG）解码在不同受试者之间泛化能力的深度学习技术。文章讨论了高受试者间变异性带来的挑战，这种变异性会在训练数据和测试数据之间产生领域迁移。文章将现有方法分为特征对齐、对抗学习、特征解耦和对比学习等几类，并讨论了理论局限性和脑电图基础模型的潜力。 AI

影响对用于跨主题脑电图解码的深度学习方法进行了结构化概述，强调了挑战和基础模型等未来方向。

排序理由这是一篇关于脑电图解码深度学习方法的综述文章。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Taida Li, Yujun Yan, Fei Dou, Wenzhan Song, Xiang Zhang · 2026-05-01 04:00

跨学科泛化用于脑电图解码：深度学习方法综述

arXiv:2604.27033v1 Announce Type: new Abstract: Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learni…