用双路径模型从脑电数据解码3D物体的形状与朝向。
Dual-Stream EEG Decoding for 3D Visual Perception
- 模仿视觉通路设计双模块，分别解码物体身份和空间朝向。
- 实现脑电到3D形状的重建，角度预测准确率高。
- 揭示大脑不同区域动态参与，非传统单一通路主导。
本文提出一种新型脑解码模型，通过双路径架构模拟生物视觉系统，实现对3D形状感知的解码。该方法在物体连续旋转过程中，分别构建基于腹侧通路（ventral）和背侧通路（dorsal）的解码模块，分别处理物体身份与空间朝向。采用圆形回归进行角度预测，并开发基于脑电条件的多视角扩散模型实现3D重建。实验表明，该模型能有效从脑电信号中解码物体身份与空间朝向，并实现高质量3D形状重建。可解释性分析显示，腹侧、背侧及运动相关通道在时间上呈现动态参与模式，而非静态的腹侧主导，为理解神经机制提供了新视角。
原文摘要 · Abstract (English)
This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspired approach implements separate decoding modules for object identity and spatial orientation, inspired by ventral and dorsal pathways, during continuous rotations. We employ circular regression for angle prediction and develop EEG-conditioned multiview diffusion for 3D reconstruction. Our approach successfully decodes both object identity and spatial orientation from EEG signals and enables 3D reconstruction from neural activity, with interpretability analyses revealing temporally structured involvement of ventral, dorsal, and motor-related channels rather than a static ventral dominance in supporting object and angle decoding.
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