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Within-Session Reliability of fNIRS in Robot-Assisted Upper-Limb Training.
Article in En | MEDLINE | ID: mdl-38498743
ABSTRACT
Functional near-infrared spectroscopy (fNIRS) seems opportune for neurofeedback in robot-assisted rehabilitation training due to its noninvasive, less physical restriction, and no electromagnetic disturbance. Previous research has proved the cross-session reliability of fNIRS responses to non-motor tasks (e.g., visual stimuli) and fine-motor tasks (e.g., finger tapping). However, it is still unknown whether fNIRS responses remain reliable 1) in gross-motor tasks, 2) within a training session, and 3) for different training parameters. Hence, this study aimed to investigate the within-session reliability of fNIRS responses to gross-motor tasks for different training parameters. Ten healthy participants were recruited to conduct right elbow extension-flexion in three robot-assisted modes. The Passive mode was fully motor-actuated, while Active1 and Active2 modes involved active engagement with different resistance levels. FNIRS data of three identical runs were used to assess the within-session reliability in terms of the map- ( R2 ) and cluster-wise ( Roverlap ) spatial reproducibility and the intraclass correlation (ICC) of temporal features. The results revealed good spatial reliability ( R2 up to 0.69, Roverlap up to 0.68) at the subject level. Besides, the within-session temporal reliabilities of Slope, Max/Min, and Mean were between good and excellent ( ICC < 0.86). We also found that the within-session reliability was positively correlated with the intensity of the training mode, except for the temporal reliability of HbO in Active2 mode. Overall, our results demonstrated good within-session reliability of fNIRS responses, suggesting fNIRS as reliable neurofeedback for constructing closed-loop robot-assisted rehabilitation systems.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Robotics Limits: Humans Language: En Journal: IEEE Trans Neural Syst Rehabil Eng Journal subject: ENGENHARIA BIOMEDICA / REABILITACAO Year: 2024 Document type: Article Country of publication: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Robotics Limits: Humans Language: En Journal: IEEE Trans Neural Syst Rehabil Eng Journal subject: ENGENHARIA BIOMEDICA / REABILITACAO Year: 2024 Document type: Article Country of publication: Estados Unidos