Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed frame can boost classification performance up to 16.7% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.

翻译：数据不足是脑机接口（BCI）构建高性能深度学习模型长期面临的挑战。尽管众多研究团队和机构为同一BCI任务收集了大量脑电图（EEG）数据集，但由于设备异质性，跨站点共享EEG数据仍面临困难。这一挑战的重要性不容忽视，因为数据多样性对于提升模型鲁棒性具有关键作用。然而，现有研究鲜少讨论该问题，主要将关注点集中于单数据集内的模型训练（通常面向跨被试或跨会话场景）。本研究提出了一种层次化个性化联邦学习EEG解码（FLEEG）框架以突破这一挑战。该创新框架为BCI开辟了新的学习范式，使格式不同的数据集能够协同参与模型训练过程。每个客户端被分配特定数据集，并训练层次化个性化模型以管理多样化的数据格式并促进信息交换。同时，服务器协调训练过程以利用所有数据集提取的知识，从而提升整体性能。该框架在基于九组不同设备采集的EEG数据集（均执行相同运动想象（MI）任务）的MI分类中进行了评估。结果表明，该框架通过实现多数据集间的知识共享，可将分类性能提升高达16.7%，尤其对较小数据集效果显著。可视化结果也表明，所提框架能使本地模型稳定聚焦于任务相关区域，从而获得更优性能。据我们所知，这是首个解决这一重要挑战的端到端方案。