Aggregating Intrinsic Information to Enhance BCI Performance through
  Federated Learning
Rui Liu Yuanyuan Chen Anran Li Yi Ding Han Yu Cuntai Guan
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.