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Classification of motor imagery using multisource joint transfer learning.

Fei Wang1, Jingyu Ping1, Zongfeng Xu2

  • 1Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China.

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Summary
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New algorithms, multi-source joint domain adaptation (MJDA) and Riemannian adaptation (MJRA), reduce calibration time for motor imagery brain-computer interfaces (MI-BCI) by transferring knowledge from other subjects using EEG signals.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Motor imagery brain-computer interfaces (MI-BCI) decode motor intentions from electroencephalogram (EEG) signals for human-computer interaction.
  • Individual EEG patterns necessitate extensive labeled data collection for each new subject, leading to lengthy calibration phases.
  • This data requirement hinders the widespread adoption and development of MI-BCI systems.

Purpose of the Study:

  • To address the challenge of long calibration times in MI-BCI.
  • To develop novel algorithms, multi-source joint domain adaptation (MJDA) and multi-source joint Riemannian adaptation (MJRA), for efficient knowledge transfer.
  • To enable MI-BCI systems to function effectively with limited labeled data from new subjects.

Main Methods:

  • Utilized common spatial pattern with Euclidean alignment to identify suitable source subjects with similar spatial patterns to the target subject.
  • Aligned EEG trial covariance matrices in Riemannian space by mitigating subject-specific baseline variations.
  • MJDA minimized feature distribution mismatch in Riemannian tangent space, while MJRA optimized an adaptive Riemannian classifier.

Main Results:

  • Both MJDA and MJRA demonstrated superior performance compared to existing state-of-the-art methods.
  • Validated on BCI Competition IV 2a and online event-related desynchronization (ERD)-BCI datasets.
  • Showcased the effectiveness of cross-subject knowledge transfer for reducing calibration needs.

Conclusions:

  • MJDA offers a novel approach for offline analysis in MI-BCI, improving efficiency.
  • MJRA presents a significant advancement for online calibration in MI-BCI systems.
  • These methods collectively enhance the practicality and accessibility of MI-BCI technology.