Electroencephalogram (EEG)-based brain-computer interfaces face challenges in data insufficiency to train neural networks for tasks and generalizability for multiple subjects. To address the issue of data scarcity in EEG research, transfer learning using EEG founda tion models (EFMs) has recently gained attention for its ability to leverage prior knowledge. Although transfer learning with EFMs enables tasks to be performed with limited training data, their increasing size presents significant computational challenges. Parameter efficient transfer learning (PETL) methods address this computational issue by tuning only a small subset of parameters from the pre-trained model. However, existing PETL methods mostly fail to account for the high-dimensional nature of EEG data, which limits their ability to fully leverage the prior knowledge of the EFM when applied to downstream tasks. To address these challenges, we propose a novel PETL method with a TASk-relevanT fEature Focusing modULe (TASTEFUL) to transfer EFMs efficiently. TASTEFUL is designed to focus on task-relevant features and efficiently learn representations tailored for downstream tasks. We evaluated our proposed TASTEFUL on tasks using publicly available EEG datasets, demonstrating its superior performance. Finally, our work highlights TASTEFUL’s potential to enhance the practical appli cation of EFMs, marking a significant advancement in PETL for EFMs.
You follow environment set up of LaBraM and BIOT, and access the Pre-trained Parameters in following links
LaBraM: https://github.com/935963004/LaBraM BIOT: https://github.com/ycq091044/BIOT CBraMod: https://github.com/wjq-learning/cbramod
If you find our paper/code useful, please consider citing our work:
@inproceedings{jeon2025parameter,
title={Parameter-efficient transfer learning for EEG foundation models via task-relevant feature focusing},
author={Jeon, Jaehyun and Jeong, Seungwoo and Shon, Yeajin and Suk, Heung-Il},
booktitle={Proceedings of the 34th ACM International Conference on Information and Knowledge Management},
pages={1068--1077},
year={2025}
}