Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning
Università Politecnica delle Marche（马尔凯理工大学）；University of Macerata（马切拉塔大学）；Horizon Intelligence Labs（地平线智能实验室）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）
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.
Neuro-3D: Towards 3D Visual Decoding from EEG Signals
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Jiao Tong University（上海交通大学）；South China University of Technology（华南理工大学）
Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding
Shanghai Maritime University（上海海事大学）；Affiliated Lianyungang Hospital of Xuzhou Medical University（徐州医科大学附属连云港医院）；The Hong Kong Polytechnic University（香港理工大学）
Electroencephalography (EEG) has become the most significant input signal for brain computer interface (BCI) based systems. However, it is very difficult to obtain satisfactory classification accuracy due to traditional methods can not fully exploit multimodal information. Herein, we propose a novel approach to modeling cognitive events from EEG data by reducing it to a video classification problem, which is designed to preserve the multimodal information of EEG. In addition, optical flow is introduced to represent the variant information of EEG. We train a deep neural network (DNN) with convo…
Beihang University（北京航空航天大学）；The University of Sheffield（谢菲尔德大学）
Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to nonstationarity of perceptual decision processes. This paper presents a novel boosted convolutional neural networks (ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by the multiwavelet-based time-frequency (TF) causality analysis. Specifically, multiwavelet basis functions are first combined with Geweke spectral measure to obtain high-resolution TF-conditional Granger causality (CGC) representations, where a regularized orthogonal forward regression (ROFR) alg…
Visual Neural Decoding via Improved Visual-EEG Semantic Consistency
School of Computer Science and Technique, Tongji University（同济大学计算机科学与技术学院）；Shanghai Eye Disease Prevention and Treatment Center（上海市眼病防治中心）；Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology（新泽西理工学院Helen and John C. Hartmann电气与计算机工程系）
EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover
Department of Computer Science, Imam Mohammad Ibn Saud Islamic University（计算机科学系，伊玛目穆罕默德·伊本·沙特伊斯兰大学）；Department of Computer Science, King Saud University（计算机科学系，国王沙特大学）
CognitionCapturerPro: Towards High-Fidelity Visual Decoding from EEG/MEG via Multi-modal Information and Asymmetric Alignment
Visual Information Processing Laboratory, School of Electronic Engineering, Xidian University（电子科技大学信息处理实验室，电子工程学院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
System Filter-Based Common Components Modeling for Cross-Subject EEG Decoding
college of Electrical and Information Engineering, Zhengzhou University（郑州大学电气与信息工程学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
