# Visual Neural Decoding via Improved Visual-EEG Semantic Consistency

arXiv: https://arxiv.org/abs/2408.06788 · HTML: https://mlsanity.com/paper/2408.06788/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding](https://mlsanity.com/paper/2109.14084/) (2021)
- [Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning](https://mlsanity.com/paper/2203.02053/) (2022)
- [CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information](https://mlsanity.com/paper/2006.12013/) (2020)
- [CyCLIP: Cyclic Contrastive Language-Image Pretraining](https://mlsanity.com/paper/2205.14459/) (2022)
- [Generic decoding of seen and imagined objects using hierarchical visual features](https://mlsanity.com/paper/1510.06479/) (2015)
- [Generalized Multimodal ELBO](https://mlsanity.com/paper/2105.02470/) (2021)
- [Decoding Natural Images from EEG for Object Recognition](https://mlsanity.com/paper/2308.13234/) (2023)
- [Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion](https://mlsanity.com/paper/2403.07721/) (2024)
- [Deep Learning Human Mind for Automated Visual Classification](https://mlsanity.com/paper/1609.00344/) (2016)
- [Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features](https://mlsanity.com/paper/2210.06756/) (2022)
- [DreamDiffusion: Generating High-Quality Images from Brain EEG Signals](https://mlsanity.com/paper/2306.16934/) (2023)
- [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022)
- [Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning](https://mlsanity.com/paper/2303.05952/) (2023)
- [Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features](https://mlsanity.com/paper/1810.10974/) (2018)
- [BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding](https://mlsanity.com/paper/2302.12971/) (2023)
- [Object classification from randomized EEG trials](https://mlsanity.com/paper/2004.06046/) (2020)
- [Perceptogram: Reconstructing Visual Percepts and Presumptive Electrode Preference from EEG](https://mlsanity.com/paper/2404.01250/) (2024)

## Built on by (most cited first, 21 of 21)

- [Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior](https://mlsanity.com/paper/2503.04207/) (2025)
- [NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment](https://mlsanity.com/paper/2511.06836/) (2025)
- [NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models](https://mlsanity.com/paper/2410.00712/) (2024)
- [Let EEG Models Learn EEG](https://mlsanity.com/paper/2605.21280/) (2026)
- [Aligning What EEG Can See: Structural Representations for Brain-Vision Matching](https://mlsanity.com/paper/2603.07077/) (2026)
- [HyFI: Hyperbolic Feature Interpolation for Brain-Vision Alignment](https://mlsanity.com/paper/2603.22721/) (2026)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval](https://mlsanity.com/paper/2604.17782/) (2026)
- [Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG](https://mlsanity.com/paper/2509.24761/) (2025)
- [Learning Brain Representation with Hierarchical Visual Embeddings](https://mlsanity.com/paper/2602.07495/) (2026)
- [CognitionCapturerPro: Towards High-Fidelity Visual Decoding from EEG/MEG via Multi-modal Information and Asymmetric Alignment](https://mlsanity.com/paper/2603.12722/) (2026)
- [Unveiling Deep Semantic Uncertainty Perception for Language-Anchored Multi-modal Vision-Brain Alignment](https://mlsanity.com/paper/2511.04078/) (2025)
- [Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding](https://mlsanity.com/paper/2605.04680/) (2026)
- [BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2508.18187/) (2025)
- [SIMON: Saliency-aware Integrative Multi-view Object-centric Neural Decoding](https://mlsanity.com/paper/2605.00401/) (2026)
- [Neural Visual Decoding via Cognitive guided Adaptive Blurring and Information Constrained Alignment](https://mlsanity.com/paper/2605.16418/) (2026)
- [Neuroscience-inspired Staged Representation Learning with Disentangled Coarse- and Fine-Grained Semantics for EEG Visual Decoding](https://mlsanity.com/paper/2605.16923/) (2026)
- [CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook](https://mlsanity.com/paper/2605.18257/) (2026)
- [SUP-MCRL: Subject-aware Unified Pseudo-feature Coded Multimodal Contrastive Representation Learning for EEG Visual Decoding](https://mlsanity.com/paper/2606.16615/) (2026)
- [FSDBN: Foreground-Aware EEG-Visual Alignment via Dynamic Brain Networks](https://mlsanity.com/paper/2607.18344/) (2026)
- [Adaptive Cortically Constrained EEG-Vision Alignment for Zero-Shot Brain-to-Image Retrieval](https://mlsanity.com/paper/2609.24109/) (2026)

## Abstract (arXiv)

Visual neural decoding aims to extract and interpret original visual experiences directly from human brain activity. Recent studies have demonstrated the feasibility of decoding visual semantic categories from electroencephalography (EEG) signals, among which metric learning-based approaches have delivered promising results. However, these methods that directly map EEG features into a pre-trained embedding space inevitably introduce mapping bias, resulting in a modality gap and semantic inconsistency that impair cross-modal alignment. To address these issues, this work constructs a Visual-EEG Joint Semantic Space to bridge the gap between visual images and neural signals. Building upon this space, we propose two novel approaches to improve semantic consistency between cross-modal representations and facilitate optimal alignment. Specifically, (1) we introduce a Visual-EEG Semantic Decoupling Network (VE-SDN) to explicitly disentangle semantic components from modality representations, thereby achieving purely semantic-level cross-modal alignment. (2) We introduce a Neural-Guided Intra-Class Consistency (NGIC) objective, an asymmetric representation alignment strategy designed to effectively enhance the robustness of visual representations and further boost decoding performance. Extensive experiments on a large-scale Visual-EEG dataset validate the effectiveness of the proposed method. Compared to the strongest baseline, our approach demonstrates superior decoding performance, yielding relative Top-1/Top-5 accuracy improvements of 38.9%/17.9% in intra-subject and 16.1%/11.3% in inter-subject settings. The code is available at https://github.com/hzalanchen/Cross-Modal-EEG