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脑视觉:用于视觉内容检索和重建的跨域EEG解码

Ting Xu¹, Lianzhi Yu¹, Yongwei Zheng¹

¹University of Shanghai for Science and Technology, Shanghai, 200093, China.

|August 15, 2025

概括

脑视觉集成视觉和情绪大脑数据,用于先进的视觉内容生成. 这种跨领域的方法增强了大脑解码,以获得更好的脑计算机接口.

科学领域:

神经科学和人工智能人工智能
大脑与计算机的接口
机器学习机器学习

背景情况:

从大脑信号解读人类视觉意图是具有挑战性的,因为孤立的数据集和有限的概括.
目前的大脑解码方法很难整合来自不同神经源和模式的信息.

研究的目的:

介绍BrainVision,这是一个跨领域学习的新框架,可以弥合视觉识别和情绪电脑图 (EEG) 数据集.
通过将来自异质EEG源的神经模式调整为共享的表示空间,使全面的视觉内容生成成为可能.

主要方法:

实施了统一的跨领域对齐策略,从THINGS-EEG和DEAP数据集绘制神经模式.
通过内容检索启用视觉内容生成,通过适配器增强的LLM进行语言描述,并使用稳定扩散模型进行图像重建.

主要成果:

脑视觉显著优于单域方法,检索准确度增加了15.3%,图像重建得到了12.7%的改进.
证明了强大的零射击泛化,在未见的刺激上保持了82%的性能.
多模输出提供了对神经活动的互补解释,增强了对视觉意图的理解.

结论:

整合各种神经数据集可以大大提高大脑的解码能力.
脑视觉为开发更直观和多功能脑机接口提供了一个有前途的方向.
该框架弥合了神经活动与跨越认知领域的丰富视觉体验之间的差距.

关键词:

跨领域的学习.电脑脑电图 (EEG) 是一种电脑电图.大型语言模型.这是一次零射击.

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