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Stroke is a major cause of death and disability worldwide. Motor-Imagery based Brain-Computer Interface (MI-BCI) models offer a post-stroke rehabilitation option. Existing studies for MI-BCI use Transfer Learning techniques like Euclidean Alignment (EA) but lose important brain information due to bandpass filtering. This study introduces new BCI architecture with multi-band temporal filters and EA. The methods considered here are Filter Bank (FB), Empirical Mode Decomposition (EMD), and Continuous Wavelet Transform (CWT). Results show performance improvements, especially with EA being applied before Filter Bank. These models offer promise for post-stroke rehabilitation, particularly when using EA before the multi-band filter.

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Euclidean Alignment for Transfer Learning in Multi-band Common Spatial Pattern

  • 会议论文

  • 发表日期：

    2025年

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  • Marcelo M. Amorim,
  • Leonardo Prata,
  • João Stephan Maurício,
  • Alex Borges,
  • Heder Bernardino,
  • Gabriel de Souza

摘要

Stroke is a major cause of death and disability worldwide. Motor-Imagery based Brain-Computer Interface (MI-BCI) models offer a post-stroke rehabilitation option. Existing studies for MI-BCI use Transfer Learning techniques like Euclidean Alignment (EA) but lose important brain information due to bandpass filtering. This study introduces new BCI architecture with multi-band temporal filters and EA. The methods considered here are Filter Bank (FB), Empirical Mode Decomposition (EMD), and Continuous Wavelet Transform (CWT). Results show performance improvements, especially with EA being applied before Filter Bank. These models offer promise for post-stroke rehabilitation, particularly when using EA before the multi-band filter.