license: mit
language:
- en
tags:
- neuroscience
- MEG
- large-brainwave-models
- time-series
MEG-GPT
MEG-GPT: A transformer-based foundation model for magnetoencephalography data
MEG-GPT is a transformer-based foundation model for human magnetoencephalography (MEG) data. As a Large Brainwave Model (LBM), it is designed to capture the spatiotemporal structure of large-scale brain dynamics. MEG-GPT is built on a decoder-only transformer trained in an autoregressive manner.
Github | Model Download | Arxiv Paper Link
📝 Usage
The easiest way to load the model and run inference is via the osl-foundation Python package. Installation and setup instructions are available in our GitHub repostiory.
Once the package is installed, you can follow the steps below.
Step 1: Clone the repository
From your command line:
git clone https://huggingface.co/OHBA-analysis/MEG-GPT models
cd models
git lfs install --local
git lfs pull
Step 2: Load the models
In Python script:
from osl_foundation import load_model
tokenizer = load_model("tokenizer")
meg_gpt = load_model("meg-gpt", checkpoint="latest")
⚙️ System Requirements
Hardware Requirements
GPU: NVIDIA GPU with CUDA support
Note: For model training and experiments, we used two NVIDIA A100 or V100 GPUs.
Software Requirements
- Python: Python 3.10
- CUDA: CUDA 11.7.0 (with TensorFlow 2.11.0)
📖 Citation
@article{huang2025,
title={MEG-GPT: A transformer-based foundation model for magnetoencephalography data},
author={Rukuang Huang, SungJun Cho, Chetan Gohil, Oiwi Parker Jones, Mark Woolrich},
year={2025},
url={https://arxiv.org/abs/2510.18080},
}