# MEL: Coordinate-Preserving EEG Tokenization for fMRI Translation

arXiv: https://arxiv.org/abs/2608.29304 · HTML: https://mlsanity.com/paper/2608.29304/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG](https://mlsanity.com/paper/2208.02405/) (2022)
- [BIOT: Cross-data Biosignal Learning in the Wild](https://mlsanity.com/paper/2305.10351/) (2023)
- [CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation](https://mlsanity.com/paper/2408.00777/) (2024)
- [NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping](https://mlsanity.com/paper/2410.05341/) (2024)
- [Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion](https://mlsanity.com/paper/2409.19130/) (2024)
- [EEG to fMRI Synthesis: Is Deep Learning a candidate?](https://mlsanity.com/paper/2009.14133/) (2020)
- [EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships](https://mlsanity.com/paper/2203.03481/) (2022)
- [Leveraging sinusoidal representation networks to predict fMRI signals from EEG](https://mlsanity.com/paper/2311.04234/) (2023)
- [fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships](https://mlsanity.com/paper/2211.02024/) (2022)
- [NT-ViT: Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis](https://mlsanity.com/paper/2409.11836/) (2024)
- [From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis](https://mlsanity.com/paper/2502.08025/) (2025)
- [Latent Representation Learning for Multimodal Brain Activity Translation](https://mlsanity.com/paper/2409.18462/) (2024)
- [Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction](https://mlsanity.com/paper/2509.11713/) (2025)
- [Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net](https://mlsanity.com/paper/2505.09521/) (2025)
- [UrbanFM: Scaling Urban Spatio-Temporal Foundation Models](https://mlsanity.com/paper/2602.20677/) (2026)

## Abstract (arXiv)

Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state monitoring, and multimodal neural decoding, because it aims to infer spatially organized hemodynamic activity from fast and accessible electrophysiological recordings. Existing EEG-to-fMRI studies mainly pursue stronger decoders, but the problem is also constrained by a representation-interface mismatch: fMRI responses are delayed, temporally integrated, and spatially distributed, whereas generic EEG encodings often entangle temporal lag, channel identity, and frequency-band structure. We propose Multi-band EEG Latent-state Tokenization (MEL), a coordinate-preserving EEG representation framework that anchors each target fMRI response to its preceding EEG history and organizes it into lag-channel-frequency neural-state tokens. By explicitly capturing hemodynamic latency and spectral-spatial dynamics, MEL aligns fMRI-pertinent EEG representations with capacity-controlled readouts without depending entirely on model scaling. Experiments on VU EEG-fMRI benchmarks and external Oddball data show that MEL improves prediction over strong NeuroBOLT baselines. Ablations and controls further indicate that the gains come from structured EEG representation rather than leakage, shortcut statistics, or decoder capacity.