Federated Transfer Learning for EEG Signal Classification

By Ce Ju, Dashan Gao, Ravikiran Mane, Ben Tan, Yang Liu, Cuntai Guan. 2020

131 citations recorded by the source corpus.

The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns associated with EEG signals limit the possibility of constructing a large EEG-BCI dataset by the conglomeration of multiple small ones for jointly training machine learning models. Hence, in this paper, we propose a novel privacy-preserving DL architecture named federated transfer learning (FTL) for EEG classification that is based on the federated learning framework. Working with the single-trial covariance matrix, the proposed architecture extracts common discriminative information from multi-subject EEG data with the help of domain adaptation techniques. We evaluate the performance of the proposed architecture on the PhysioNet dataset for 2-class motor imagery classification. While avoiding the actual data sharing, our FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. Also, in the absence of multi-subject data, our architecture provides 6% better accuracy compared to other state-of-the-art DL architectures.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

  1. Ramesh Raskar
  2. Qiang Yang
  3. Ben Tan
  4. Dashan Gao
  5. Yang Liu
  6. Ce Ju

In Electrical and Electronic Engineering

  1. Jinhyun So
  2. Murali Annavaram
  3. Ramesh Raskar
  4. Yan Kang
  5. Li Shen
  6. Qiang Yang
  7. Abhishek Singh
  8. Hongyi Wang

Citations in the corpus