---
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}
}
```