# Neural Brain Fields: A NeRF-Inspired Approach for Generating Nonexistent EEG Electrodes arXiv: https://arxiv.org/abs/2601.00012 ยท HTML: https://mlsanity.com/paper/2601.00012/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 54 references that are in the map. - First posted on arXiv 20 Dec 2025. ## Builds on (most cited first, 27 of 27) - [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022) - [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020) - [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021) - [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021) - [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018) - [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021) - [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016) - [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021) - [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021) - [NeRF-Editing: Geometry Editing of Neural Radiance Fields](https://mlsanity.com/paper/2205.04978/) (2022) - [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022) - [Data augmentation for learning predictive models on EEG: a systematic comparison](https://mlsanity.com/paper/2206.14483/) (2022) - [EEG2IMAGE: Image Reconstruction from EEG Brain Signals](https://mlsanity.com/paper/2302.10121/) (2023) - [EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals](https://mlsanity.com/paper/2106.09135/) (2021) - [ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification](https://mlsanity.com/paper/1802.00308/) (2018) - [Learning Robust Deep Visual Representations from EEG Brain Recordings](https://mlsanity.com/paper/2310.16532/) (2023) - [LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis](https://mlsanity.com/paper/2510.22257/) (2025) - [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023) - [EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification](https://mlsanity.com/paper/2407.20254/) (2024) - [On The Effects Of Data Normalisation For Domain Adaptation On EEG Data](https://mlsanity.com/paper/2210.01081/) (2022) - [Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models](https://mlsanity.com/paper/2410.02780/) (2024) - [Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks](https://mlsanity.com/paper/2502.08803/) (2025) - [SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation](https://mlsanity.com/paper/2009.01381/) (2020) - [Decoding EEG Speech Perception with Transformers and VAE-based Data Augmentation](https://mlsanity.com/paper/2501.04359/) (2025) - [EEG-Driven 3D Object Reconstruction with Style Consistency and Diffusion Prior](https://mlsanity.com/paper/2410.20981/) (2024) - [Spatial-Temporal Mamba Network for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2409.09627/) (2024) - [3D-Telepathy: Reconstructing 3D Objects from EEG Signals](https://mlsanity.com/paper/2506.21843/) (2025) ## Built on by (most cited first, 2 of 2) - [EMAG: Differentiable 4D Gaussian Mixture Splatting for EEG Spatial Super-Resolution](https://mlsanity.com/paper/2605.29731/) (2026) - [Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation](https://mlsanity.com/paper/2606.23707/) (2026) ## Abstract (arXiv) Electroencephalography (EEG) data present unique modeling challenges because recordings vary in length, exhibit very low signal to noise ratios, differ significantly across participants, drift over time within sessions, and are rarely available in large and clean datasets. Consequently, developing deep learning methods that can effectively process EEG signals remains an open and important research problem. To tackle this problem, this work presents a new method inspired by Neural Radiance Fields (NeRF). In computer vision, NeRF techniques train a neural network to memorize the appearance of a 3D scene and then uses its learned parameters to render and edit the scene from any viewpoint. We draw an analogy between the discrete images captured from different viewpoints used to learn a continuous 3D scene in NeRF, and EEG electrodes positioned at different locations on the scalp, which are used to infer the underlying representation of continuous neural activity. Building on this connection, we show that a neural network can be trained on a single EEG sample in a NeRF style manner to produce a fixed size and informative weight vector that encodes the entire signal. Moreover, via this representation we can render the EEG signal at previously unseen time steps and spatial electrode positions. We demonstrate that this approach enables continuous visualization of brain activity at any desired resolution, including ultra high resolution, and reconstruction of raw EEG signals. Finally, our empirical analysis shows that this method can effectively simulate nonexistent electrodes data in EEG recordings, allowing the reconstructed signal to be fed into standard EEG processing networks to improve performance.