A Personalized Multimodal BCI–Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients DOI Creative Commons
Brian Premchand, Zhuo Zhang, Kai Keng Ang

et al.

Biomimetics, Journal Year: 2025, Volume and Issue: 10(2), P. 94 - 94

Published: Feb. 7, 2025

Multimodal brain-computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We novel method of personalizing rehabilitation by aligning each patient's specific abilities treatment options available. collected 160 single trials motor imagery using 10 healthy participants. identified confounding effect respiration in fNIRS signal data collected. Hence, incorporate breathing sensor synchronize (MI) cues participant's respiratory cycle. found implementing synchronization (RS) resulted less dispersed readings oxyhemoglobin (HbO). then conducted clinical trial on BCI-SR Four chronic patients were recruited undergo 6 weeks rehabilitation, three times per week, whereby primary outcome was measured upper-extremity Fugl-Meyer Motor Assessment (FMA) Action Research Arm Test (ARAT) scores 0, 6, 12. The results showed striking coherence activation patterns EEG across all patients. addition, FMA ARAT significantly improved week 12 relative pre-trial baseline, mean gains 8.75 ± 1.84 5.25 2.17, respectively (mean SEM). These improvements better than Standard Therapy group when retrospectively compared previous trials. suggest leads performance standard BCI-SR, synchronizing increased consistency HbO levels, leading proposed holds promise engage promote neuroplasticity improvements.

Language: Английский

A Personalized Multimodal BCI–Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients DOI Creative Commons
Brian Premchand, Zhuo Zhang, Kai Keng Ang

et al.

Biomimetics, Journal Year: 2025, Volume and Issue: 10(2), P. 94 - 94

Published: Feb. 7, 2025

Multimodal brain-computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We novel method of personalizing rehabilitation by aligning each patient's specific abilities treatment options available. collected 160 single trials motor imagery using 10 healthy participants. identified confounding effect respiration in fNIRS signal data collected. Hence, incorporate breathing sensor synchronize (MI) cues participant's respiratory cycle. found implementing synchronization (RS) resulted less dispersed readings oxyhemoglobin (HbO). then conducted clinical trial on BCI-SR Four chronic patients were recruited undergo 6 weeks rehabilitation, three times per week, whereby primary outcome was measured upper-extremity Fugl-Meyer Motor Assessment (FMA) Action Research Arm Test (ARAT) scores 0, 6, 12. The results showed striking coherence activation patterns EEG across all patients. addition, FMA ARAT significantly improved week 12 relative pre-trial baseline, mean gains 8.75 ± 1.84 5.25 2.17, respectively (mean SEM). These improvements better than Standard Therapy group when retrospectively compared previous trials. suggest leads performance standard BCI-SR, synchronizing increased consistency HbO levels, leading proposed holds promise engage promote neuroplasticity improvements.

Language: Английский

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