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

被引：0

作者：

Amorim, Marcelo M. ^{[1
]}

Prata, Leonardo ^{[1
]}

Mauricio, Joao Stephan ^{[1
]}

Borges, Alex ^{[1
]}

Bernardino, Heder ^{[1
]}

de Souza, Gabriel ^{[1
]}

机构：

[1] Fed Univ Juiz de Fora UFJF, Juiz De Fora, MG, Brazil

来源：

INTELLIGENT SYSTEMS, BRACIS 2024, PT I | 2025年 / 15412卷

关键词：

Electroencephalogram; Motor Imagery; Brain-Machine Interface; Medical applications; BRAIN-COMPUTER INTERFACES; ALGORITHMS; SYSTEM;

D O I：

10.1007/978-3-031-79029-4_30

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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 poststroke rehabilitation, particularly when using EA before the multi-band filter.

引用

页码：430 / 443

页数：14

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