# NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery

arXiv: https://arxiv.org/abs/2506.06898 · HTML: https://mlsanity.com/paper/2506.06898/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 76 references that are in the map.
- First posted on arXiv 7 Jun 2025.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images](https://mlsanity.com/paper/2011.10650/) (2020)
- [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://mlsanity.com/paper/2211.08332/) (2022)
- [Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors](https://mlsanity.com/paper/2305.18274/) (2023)
- [Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding](https://mlsanity.com/paper/2211.06956/) (2022)
- [MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data](https://mlsanity.com/paper/2403.11207/) (2024)
- [Natural scene reconstruction from fMRI signals using generative latent diffusion](https://mlsanity.com/paper/2303.05334/) (2023)
- [Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity](https://mlsanity.com/paper/2305.11675/) (2023)
- [Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs](https://mlsanity.com/paper/2202.12692/) (2022)
- [Decoding natural image stimuli from fMRI data with a surface-based convolutional network](https://mlsanity.com/paper/2212.02409/) (2022)
- [DREAM: Visual Decoding from Reversing Human Visual System](https://mlsanity.com/paper/2310.02265/) (2023)
- [UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activity](https://mlsanity.com/paper/2308.07428/) (2023)
- [Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities](https://mlsanity.com/paper/2305.17214/) (2023)
- [Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs](https://mlsanity.com/paper/2306.11536/) (2023)
- [Through their eyes: multi-subject Brain Decoding with simple alignment techniques](https://mlsanity.com/paper/2309.00627/) (2023)
- [DCNN-GAN: Reconstructing Realistic Image from fMRI](https://mlsanity.com/paper/1901.07368/) (2019)
- [Spurious reconstruction from brain activity](https://mlsanity.com/paper/2405.10078/) (2024)
- [Aligning brain functions boosts the decoding of visual semantics in novel subjects](https://mlsanity.com/paper/2312.06467/) (2023)
- [Reconstructing seen images from human brain activity via guided stochastic search](https://mlsanity.com/paper/2305.00556/) (2023)
- [Brain-optimized inference improves reconstructions of fMRI brain activity](https://mlsanity.com/paper/2312.07705/) (2023)
- [Second Sight: Using brain-optimized encoding models to align image distributions with human brain activity](https://mlsanity.com/paper/2306.00927/) (2023)
- [A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision](https://mlsanity.com/paper/2503.06286/) (2025)

## Built on by (most cited first, 8 of 8)

- [Towards Interpretable Visual Decoding with Attention to Brain Representations](https://mlsanity.com/paper/2509.23566/) (2025)
- [ENIGMA: EEG-to-Image in 15 Minutes Using Less Than 1% of the Parameters](https://mlsanity.com/paper/2602.10361/) (2026)
- [Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning](https://mlsanity.com/paper/2604.08068/) (2026)
- [Seeing the imagined: latent functional alignment in visual imagery decoding from fMRI data](https://mlsanity.com/paper/2604.15374/) (2026)
- [MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery](https://mlsanity.com/paper/2605.17198/) (2026)
- [Real-time Reconstruction of Human Visual Perception from fMRI](https://mlsanity.com/paper/2607.22753/) (2026)
- [EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits](https://mlsanity.com/paper/2607.27857/) (2026)
- [Neurons++: A Unified Visual-Stream Framework for Image and Video Reconstruction from fMRI with Neural-to-Visual Interpretability](https://mlsanity.com/paper/2610.13763/) (2026)

## Abstract (arXiv)

We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI activity paired with seen images that enabled unprecedented improvements in fMRI-to-image reconstruction efforts. Recent models trained on NSD have been evaluated only on seen image reconstruction. Using NSD-Imagery, it is possible to assess how well these models perform on mental image reconstruction. This is a challenging generalization requirement because mental images are encoded in human brain activity with relatively lower signal-to-noise and spatial resolution; however, generalization from seen to mental imagery is critical for real-world applications in medical domains and brain-computer interfaces, where the desired information is always internally generated. We provide benchmarks for a suite of recent NSD-trained open-source visual decoding models (MindEye1, MindEye2, Brain Diffuser, iCNN, Takagi et al.) on NSD-Imagery, and show that the performance of decoding methods on mental images is largely decoupled from performance on vision reconstruction. We further demonstrate that architectural choices significantly impact cross-decoding performance: models employing simple linear decoding architectures and multimodal feature decoding generalize better to mental imagery, while complex architectures tend to overfit visual training data. Our findings indicate that mental imagery datasets are critical for the development of practical applications, and establish NSD-Imagery as a useful resource for better aligning visual decoding methods with this goal.