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Aggregating intrinsic information to enhance BCI performance through federated learning
Rui Liu1, Yuanyuan Chen1, Anran Li1
1School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
Summary
Federated Learning EEG decoding (FLEEG) enables diverse brain-computer interface datasets to collaborate, overcoming device heterogeneity. This boosts deep learning model performance by enabling knowledge sharing, especially for smaller datasets.
Area of Science:
- Neuroscience and Artificial Intelligence
- Brain-Computer Interface (BCI) research
- Machine Learning for signal processing
Background:
- High-performance deep learning models for Brain-Computer Interfaces (BCI) are hindered by insufficient and heterogeneous Electroencephalography (EEG) data.
- Existing BCI research often focuses on single-dataset training, neglecting the potential of multi-site data due to device diversity.
- Data diversity is crucial for developing robust BCI models, yet sharing multi-site EEG data remains a significant challenge.
Purpose of the Study:
- To propose a novel framework addressing the challenge of training deep learning models with heterogeneous EEG data from multiple sources.
- To enable collaborative model training across disparate EEG datasets, enhancing data diversity and model robustness in BCI.
- To introduce a new learning paradigm for BCI that overcomes device heterogeneity and facilitates knowledge exchange.
Main Methods:
- Development of a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework.
- Each client dataset trains a personalized hierarchical model to manage diverse data formats and enable information exchange.
- A central server coordinates training, aggregating knowledge from all datasets to improve overall performance.
Main Results:
- The FLEEG framework demonstrated improved Motor Imagery (MI) classification performance by up to 8.4% across nine diverse EEG datasets.
- Knowledge sharing enabled by FLEEG significantly benefits smaller datasets, enhancing their contribution to model training.
- Visualization confirmed that FLEEG-trained models maintain a stable focus on task-related neural areas, leading to better classification accuracy.
Conclusions:
- The proposed FLEEG framework offers an effective end-to-end solution for leveraging multi-site, heterogeneous EEG data in BCI.
- Federated learning with hierarchical personalization can successfully address data heterogeneity challenges in BCI model development.
- This approach represents a significant advancement in BCI research, paving the way for more robust and generalizable deep learning models.
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