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Ensemble or pool: A comprehensive study on transfer learning for c-VEP BCI during interpersonal interaction.

Zhihua Huang1, Wenming Zheng2, Yingjie Wu1

  • 1College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China.

Journal of Neuroscience Methods
|July 10, 2020
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Summary

Transfer learning in brain-computer interfaces (BCI) significantly reduces calibration time. This study validates that transferring knowledge between subjects improves BCI performance, even with differing brain signal distributions.

Keywords:
Brain–computer interfaceCode-modulated visual evoked potentialSubject transfer frameworkTransfer learning

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCI) require extensive calibration.
  • Transfer learning is being explored to reduce BCI calibration time and enable zero-training BCI.

Purpose of the Study:

  • To comprehensively investigate transfer learning performance in BCI.
  • To identify key factors influencing transfer learning effectiveness in BCI.

Main Methods:

  • Proposed two novel transfer learning approaches: whole-channel and corresponding-channel transfer.
  • Developed a subject transfer framework combining ensemble and pool strategies.
  • Evaluated eight framework implementations on a code-modulated visual evoked potential (c-VEP) BCI dataset from a 'Chicken Game' experiment.

Main Results:

  • Transfer learning generally yields acceptable classification performance in c-VEP BCI.
  • Analysis revealed significant differences in brain signal distribution between target and source subjects.
  • This finding supports the underlying hypothesis of knowledge transfer in BCI research.

Conclusions:

  • Transfer learning effectively reduces calibration time for c-VEP BCI.
  • Enables BCI recognition even with limited subject-specific data.
  • Provides strong evidence for the validity of transferring knowledge in BCI.