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Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.

Zhuo Cai1, Yunyuan Gao1, Feng Fang2

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China.

Journal of Neuroscience Methods
|November 30, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) for brain-computer interfaces. MTICSP effectively addresses subject-specific differences in motor imagery decoding, significantly improving accuracy across various datasets.

Keywords:
Common spatial patternsEEGMultiple source domainsTransfer learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCIs) face challenges in decoding algorithms due to inter-subject variability in brain structure and imaging.
  • Transfer learning (TL) is applied to BCIs to mitigate these differences, but existing methods struggle with domain alignment, feature extraction, and weight allocation.

Purpose of the Study:

  • To propose a novel Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) to enhance motor imagery (MI) decoding accuracy in BCIs.
  • To address limitations in current TL approaches for MI, including inconsistent domain alignment and ineffective feature extraction.

Main Methods:

  • Implemented a Multi-layer transfer learning algorithm (MTICSP) incorporating improved Common Spatial Patterns (CSP).
  • Utilized Target Alignment (TA) for initial source and target domain data alignment, reducing distribution differences.
  • Applied re-weighting of mean covariance matrices based on trial-wise distances between domains.
  • Introduced a regularization coefficient into CSP for enhanced feature extraction and domain difference reduction.
  • Employed Joint Distribution Adaptation (JDA) for final alignment of feature blocks between source and target domains.

Main Results:

  • MTICSP demonstrated significant effectiveness on public datasets in multi-source to single-target (MTS) and single-source to single-target (STS) paradigms.
  • Achieved high accuracies, e.g., 80.21% (MTS) and 77.58% (STS) on a 5-person dataset, and 80.10% (MTS) and 73.91% (STS) on a 9-person dataset.
  • Outperformed other state-of-the-art algorithms in comparative experiments.
  • Validated generalization on a self-collected fatigue EEG dataset, yielding 94.83% (MTS) and 87.41% (STS) accuracy.

Conclusions:

  • The proposed MTICSP algorithm effectively combines improved CSP with transfer learning for robust feature extraction in BCIs.
  • MTICSP offers a promising new approach for improving motor imagery decoding by addressing inter-subject variability.
  • The method shows superior performance and generalization capabilities, advancing the field of transfer learning in BCIs.