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Multi-source domain adaptation for decoder calibration of intracortical brain-machine interface.

Wei Li1, Shaohua Ji1, Xi Chen1

  • 1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China.

Journal of Neural Engineering
|October 27, 2020
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Summary
This summary is machine-generated.

This study introduces a multi-source domain adaptation algorithm for intracortical brain-machine interfaces (iBMIs). It improves decoding performance and reduces calibration time by using historical data, outperforming single-source methods.

Keywords:
decoder calibrationdomain adaptationintracortical brain-machine interfacemulti-source domain adaptation

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Intracortical brain-machine interfaces (iBMIs) require daily decoder retraining due to neural recording nonstationarity.
  • Existing domain adaptation (DA) methods in iBMIs often use single source domains, leading to performance instability.
  • Efficient decoder calibration is crucial for the clinical application of iBMIs.

Purpose of the Study:

  • To develop a multi-source domain adaptation (DA) algorithm for iBMIs to enhance decoding performance and reduce calibration time.
  • To leverage historical neural data more effectively than single-source DA approaches.
  • To address the challenge of decoder recalibration in iBMIs.

Main Methods:

  • A principal component analysis (PCA)-based multi-source domain adaptation (PMDA) algorithm was developed.
  • Feature transfer was employed to minimize discrepancies between target and multiple source domains.
  • Multiple weighted sub-classifiers were constructed using multi-source historical data and current samples.

Main Results:

  • The proposed PMDA algorithm demonstrated superior and more robust decoding performance compared to existing methods.
  • The algorithm effectively reduced decoder calibration time by utilizing a small current sample set.
  • Multi-day historical data significantly improved decoding accuracy and stability.

Conclusions:

  • Multi-source DA offers a promising solution to reduce time-consuming daily retraining in iBMIs.
  • Utilizing multi-day historical data enhances decoding performance and robustness in iBMIs.
  • The developed algorithm is efficient and has significant potential for clinical translation.