# Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG

arXiv: https://arxiv.org/abs/2509.24761 · HTML: https://mlsanity.com/paper/2509.24761/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 60 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [OmniVL:One Foundation Model for Image-Language and Video-Language Tasks](https://mlsanity.com/paper/2209.07526/) (2022)
- [Generic decoding of seen and imagined objects using hierarchical visual features](https://mlsanity.com/paper/1510.06479/) (2015)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Mind Reader: Reconstructing complex images from brain activities](https://mlsanity.com/paper/2210.01769/) (2022)
- [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)
- [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022)
- [Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features](https://mlsanity.com/paper/1810.10974/) (2018)
- [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022)
- [LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface](https://mlsanity.com/paper/2105.02786/) (2021)
- [Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior](https://mlsanity.com/paper/2503.04207/) (2025)
- [EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals](https://mlsanity.com/paper/2106.09135/) (2021)
- [Visual Neural Decoding via Improved Visual-EEG Semantic Consistency](https://mlsanity.com/paper/2408.06788/) (2024)
- [Object classification from randomized EEG trials](https://mlsanity.com/paper/2004.06046/) (2020)
- [Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts](https://mlsanity.com/paper/2209.11233/) (2022)
- [Graph-Aware Contrasting for Multivariate Time-Series Classification](https://mlsanity.com/paper/2309.05202/) (2023)

## Built on by (most cited first, 3 of 3)

- [SUP-MCRL: Subject-aware Unified Pseudo-feature Coded Multimodal Contrastive Representation Learning for EEG Visual Decoding](https://mlsanity.com/paper/2606.16615/) (2026)
- [What Does the Brain See? Multiview Neural Representations to Demystify the Brain-Visual Alignment](https://mlsanity.com/paper/2606.25718/) (2026)
- [FSDBN: Foreground-Aware EEG-Visual Alignment via Dynamic Brain Networks](https://mlsanity.com/paper/2607.18344/) (2026)

## Abstract (arXiv)

Decoding visual neural representations from Electroencephalography (EEG) signals remains a formidable challenge due to their high-dimensional, noisy, and non-Euclidean nature. In this work, we propose a Spatial-Functional Awareness Transformer-based Graph Archetype Contrastive Learning (SFTG) framework to enhance EEG-based visual decoding. Specifically, we introduce the EEG Graph Transformer (EGT), a novel graph-based neural architecture that simultaneously encodes spatial brain connectivity and temporal neural dynamics. To mitigate high intra-subject variability, we propose Graph Archetype Contrastive Learning (GAC), which learns subject-specific EEG graph archetypes to improve feature consistency and class separability. Furthermore, we conduct comprehensive subject-dependent and subject-independent evaluations on the Things-EEG dataset, demonstrating that our approach significantly outperforms prior state-of-the-art EEG decoding methods.The results underscore the transformative potential of integrating graph-based learning with contrastive objectives to enhance EEG-based brain decoding, paving the way for more generalizable and robust neural representations.