无需校准的脑机接口，通过持续自适应提升精度
EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation
- 用多用户数据预训练模型，再在线微调以适应个体变化
- 在9个数据集上显著优于传统静态方法，最高提升15%
- 适合希望免校准部署的BCI研究者与开发者
脑机接口因神经信号随时间漂移且个体差异大，需频繁校准，限制实际应用。本文提出EDAPT，一种任务与模型无关的框架，通过持续在线适配实现免校准。先用多用户数据训练基础解码器，再在使用过程中通过监督微调持续个性化。在涵盖三种BCI任务的九个数据集上测试，结果表明其精度始终优于传统静态方法。性能提升主要来自群体预训练与在线持续微调的结合，部分数据集上无监督域适应进一步增益。EDAPT在消费级硬件上更新模型仅需200毫秒以内。解码精度随总数据量增长，而非数据在用户与试验间的分配方式。该方法为实现真正免校准的脑机接口提供了可行路径。
原文摘要 · Abstract (English)
Brain-computer interfaces (BCIs) suffer from accuracy degradation as neural signals drift over time and vary across users, requiring frequent recalibration that limits practical deployment. We introduce EDAPT, a task- and model-agnostic framework that eliminates calibration through continual model adaptation. EDAPT first trains a baseline decoder using data from multiple users, then continually personalizes this model via supervised finetuning as the neural patterns evolve during use. We tested EDAPT across nine datasets covering three BCI tasks, and found that it consistently improved accuracy over conventional, static methods. These improvements primarily stem from combining population-level pretraining and online continual finetuning, with unsupervised domain adaptation providing further gains on some datasets. EDAPT runs efficiently, updating models within 200 milliseconds on consumer-grade hardware. Finally, decoding accuracy scales with total data budget rather than its allocation between subjects and trials. EDAPT provides a practical pathway toward calibration-free BCIs, reducing a major barrier to BCI deployment.
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