| license: other | |
| tags: | |
| - eeg | |
| - sequence-to-sequence | |
| - in-context-learning | |
| - brain-computer-interface | |
| - large-eeg-model | |
| library_name: pytorch | |
| # ECHO | |
| **ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models** is a decoder-centric framework for EEG modeling. Instead of treating a pretrained EEG encoder as a fixed feature extractor followed by a lightweight classifier, ECHO reformulates EEG modeling as sequence-to-sequence learning over EEG signals, task tokens, label tokens, and contextual support samples. This design is intended to improve cross-task and cross-dataset generalization and to enable in-context adaptation for heterogeneous EEG tasks. | |
| <p align="center"> | |
| <img src="https://raw.githubusercontent.com/wythedee/ECHO/main/images/main.png" alt="ECHO framework overview" width="900"> | |
| </p> | |
| ## Code | |
| The official implementation is available at: | |
| ```text | |
| https://github.com/wythedee/ECHO | |
| ``` | |
| The public repository contains the encoder-decoder code structure: | |
| - `FAST/`: EEG encoder components. | |
| - `EEG2Text/`: EEG-to-text decoder components. | |
| ## Paper | |
| - arXiv: https://arxiv.org/abs/2509.22556 | |
| - OpenReview: https://openreview.net/forum?id=ClLQ6cLkoR | |
| ```bibtex | |
| @article{liu2025echo, | |
| title={ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models}, | |
| author={Liu, Chenyu and Deng, Yuqiu and Liu, Tianyu and Zhou, Jinan and Zhou, Xinliang and Jia, Ziyu and Ding, Yi}, | |
| journal={arXiv preprint arXiv:2509.22556}, | |
| year={2025} | |
| } | |
| ``` | |