![Logo](figures/logo-05.png)
Brant is a foundation model for modeling intracranial recordings, which learns powerful representations of intracranial neural signals by pre-training, as a large-scale, off-the-shelf model for medicine. Brant is the largest model in the field of brain signals and is pre-trained on a large corpus of intracranial data. The design of Brant is to capture long-term temporal dependency and spatial correlation from neural signals, combining the information in both time and frequency domains.
Brant is the largest model on brain signals and pre-trained on a large intracranial dataset collected by us. As shown in the figure below, Brant contains over 500M parameters, far more than other existing works on brain signals.
![Model scale comparison](figures/scale_compare.png)
The figure below summarizes the results of all the downstream tasks, including neural signal forecasting, frequency-phase forecasting, imputation and seizure detection. As a foundation model for intracranial recordings, compared with other baselines, Brant achieves consistent SOTA performance on a variety of tasks w.r.t. several medical scenarios, showing the great potential in neural recordings modeling.
![Overall performance of Brant and baselines](figures/overall_res.png)
In the future, by scaling up our dataset, the scale of our model can be further expanded to capture higher-level semantic information from neural data, revealing more complicated brain activities and dynamics, to provide assistance for more healthcare applications.
@inproceedings{zhang2023brant,
title={Brant: Foundation Model for Intracranial Neural Signal},
author={Zhang, Daoze and Yuan, Zhizhang and Yang, Yang and Chen, Junru and Wang, Jingjing and Li, Yafeng},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}
The data collection and experiments conducted in our work have been approved by the Institutional Review Board (IRB) and passed ethical review. All participants have signed informed consent forms.