brain3d-eeg-decoding

---
name: brain3d-eeg-decoding
description: "... 触发词: eeg, fusion, brain signal, multimodal"
---

# Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning

## 概述

Decoding visual information from electroencephalography (EEG) has recently achieved promising results, primarily focusing on reconstructing two-dimensional (2D) images from brain activity. However, the reconstruction of three-dimensional (3D) representations remains largely unexplored. This limits the geometric understanding and reduces the applicability of neural decoding in different contexts. To address this gap, we propose Brain3D, a multimodal architecture for EEG-to-3D reconstruction based on EEG-to-image decoding. It progressively transforms neural representations into the 3D domain using geometry-aware generative reasoning. Our pipeline first produces visually grounded images from EEG signals, then employs a multimodal large language model to extract structured 3D-aware descriptions, which guide a diffusion-based generation stage whose outputs are finally converted into coherent 3D meshes via a single-image-to-3D model. By decomposing the problem into structured stages, the proposed approach avoids direct EEG-to-3D mappings and enables scalable brain-driven 3D generation. We conduct a comprehensive evaluation comparing the reconstructed 3D outputs against the original visual stimuli, assessing both semantic alignment and geometric fidelity. Experimental results demonstrate strong performance of the proposed architecture, achieving up to 85.4% 10-way Top-1 EEG decoding accuracy and 0.648 CLIPScore, supporting the feasibility of multimodal EEG-driven 3D reconstruction.

## 来源论文

- **标题**: Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning
- **作者**: Emanuele Balloni, Emanuele Frontoni, Chiara Matti, Marina Paolanti, Roberto Pierdicca, Emiliano Santarnecchi
- **arXiv**: 2604.08068v1
- **发布日期**: 2026-04-09
- **类别**: cs.CV
- **PDF**: https://arxiv.org/pdf/2604.08068v1

## 核心概念

### 主要贡献
1. 提出了一种创新性的研究方法
2. 提供了理论分析和实验验证
3. 展示了在实际场景中的应用潜力

### 技术方法
- 采用机器学习方法分析神经数据
- 结合信号处理和统计建模

## 实际应用

### 应用场景
- 神经科学研究
- 脑机接口开发
- 神经形态计算
- 认知神经科学

## 相关技术

- 神经科学方法
- 机器学习/深度学习
- 信号处理
- 图神经网络

## 激活关键词

- eeg - fusion - brain signal - multimodal

## 参考

- Emanuele Balloni et al. (2026). "Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning." arXiv:2604.08068v1.

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*技能生成于: 2026-04-12*
*来源: arXiv 神经科学论文研究*