Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients

Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients

IntroductionMotor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject classification remains challenging due to limited training samples and significant inter-subject variability. This study propos...

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Bibliographic Details
Main Authors: Jin Feng, YunDe Li, ZiJun Huang, Yehang Chen, SenLiang Lu, RongLiang Hu, QingHui Hu, YuYao Chen, XiMiao Wang, Yong Fan, Jing He
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-03-01
Series:Frontiers in Human Neuroscience
Subjects:
MI-fNIRS
Cross-Subject Heterogeneous Transfer Learning Model
Bayesian Extreme Learning Machine
stroke patients
BCI
Online Access:https://www.frontiersin.org/articles/10.3389/fnhum.2025.1555690/full
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https://www.frontiersin.org/articles/10.3389/fnhum.2025.1555690/full

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