metadata
license: mit
pipeline_tag: other
tags:
- neuroscience
- brain-to-text
- speech decoding
- brain decoding
- large brain models
- brain foundation models
MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training
MEG-XL is a brain-to-text foundation model pre-trained with 2.5 minutes of MEG context per sample (equivalent to 191k tokens). It is designed to capture extended neural context, enabling high data efficiency for decoding words from brain activity.
- Paper: MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training
- Repository: GitHub - neural-processing-lab/MEG-XL
- Weights/Checkpoint: meg-xl-med.ckpt
Usage
Instructions for environment setup and data preparation are available in the official GitHub repository.
Fine-tuning MEG-XL for Brain-to-Text
You can fine-tune or evaluate the model on word decoding tasks using the following command structure:
python -m brainstorm.evaluate_criss_cross_word_classification \
--config-name=eval_criss_cross_word_classification_{armeni, gwilliams, libribrain} \
model.criss_cross_checkpoint=/path/to/your/checkpoint.ckpt
Linear Probing
To perform linear probing, use:
python -m brainstorm.evaluate_criss_cross_word_classification \
--config-name=eval_criss_cross_word_classification_linear_probe_{armeni, gwilliams, libribrain} \
model.criss_cross_checkpoint=/path/to/your/checkpoint.ckpt
Requirements
- Python >= 3.12
- High-VRAM GPU (>= 40-80GiB depending on the task).
Citation
If you find this work helpful in your research, please cite:
@article{jayalath2026megxl,
title={{MEG-XL}: Data-Efficient Brain-to-Text via Long-Context Pre-Training},
author={Jayalath, Dulhan and Parker Jones, Oiwi},
journal={arXiv preprint arXiv:2602.02494},
year={2026}
}