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Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.

Kai Zhang1,2, Guanghua Xu1,2, Xiaowei Zheng1,2

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)
|November 10, 2020
PubMed
Summary

Transfer learning (TL) significantly enhances electroencephalography (EEG) decoding accuracy across different sessions and subjects. This approach improves brain-computer interface (BCI) system calibration times by overcoming data distribution variations.

Keywords:
EEGclassificationdecodingreviewtransfer learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Electroencephalography (EEG) decoding commonly relies on machine learning, which assumes consistent data distributions.
  • Variations between sessions and subjects in EEG data violate this assumption, reducing decoding model accuracy for mental tasks.
  • Transfer learning (TL) offers a promising solution for processing EEG signals across diverse sessions and subjects.

Purpose of the Study:

  • To review the application of transfer learning (TL) in electroencephalography (EEG) decoding.
  • To categorize TL methods, EEG paradigms, and datasets used in published studies (2010-2020).
  • To discuss the current state and future directions of TL for EEG decoding.

Main Methods:

  • Systematic literature review of 80 studies on TL for EEG decoding from 2010 to 2020.
  • Categorization of TL approaches: instance knowledge, feature representation knowledge, and model parameter knowledge.
  • Analysis of EEG paradigms and datasets employed in the reviewed studies.

Main Results:

  • Transfer learning significantly improves decoding model performance across subjects and sessions.
  • TL effectively reduces the necessary calibration time for brain-computer interface (BCI) systems.
  • Identified common TL methods, EEG paradigms, and datasets utilized in the field.

Conclusions:

  • Transfer learning is a powerful technique for enhancing EEG decoding accuracy and efficiency.
  • The review provides practical insights and performance outcomes to guide future EEG research.
  • TL holds significant potential for advancing brain-computer interface (BCI) technologies.