Assessment of the Efficacy of EEG-Based MI-BCI With Visual Feedback and EEG Correlates of Mental Fatigue for Upper-Limb Stroke Rehabilitation.

Abstract

OBJECTIVE:

This single-arm multisite trial investigates the efficacy of the neurostyle brain exercise therapy towards enhanced recovery (nBETTER) system, an electroencephalogram (EEG)-based motor imagery brain-computer interface (MI-BCI) employing visual feedback for upper-limb stroke rehabilitation, and the presence of EEG correlates of mental fatigue during BCI usage.

METHODS:

A total of 13 recruited stroke patients underwent thrice-weekly nBETTER therapy coupled with standard arm therapy over six weeks. Upper-extremity Fugl-Meyer motor assessment (FMA) scores were measured at baseline (week 0), post-intervention (week 6), and follow-ups (weeks 12 and 24). In total, 11/13 patients (mean age 55.2 years old, mean post-stroke duration 333.7 days, mean baseline FMA 35.5) completed the study.

RESULTS:

Significant FMA gains relative to baseline were observed at weeks 6 and 24. Retrospectively comparing to the standard arm therapy (SAT) control group and BCI with haptic knob (BCI-HK) intervention group from a previous similar study, the SAT group had no significant gains, whereas the BCI-HK group had significant gains at weeks 6, 12, and 24. EEG analysis revealed significant positive correlations between relative beta power and BCI performance in the frontal and central brain regions, suggesting that mental fatigue may contribute to poorer BCI performance.

CONCLUSION:

nBETTER, an EEG-based MI-BCI employing only visual feedback, helps stroke survivors sustain short-term FMA improvement. Analysis of EEG relative beta power indicates that mental fatigue may be present.

SIGNIFICANCE:

This study adds nBETTER to the growing literature of safe and effective stroke rehabilitation MI-BCI, and suggests an additional fatigue-monitoring role in future such BCI.

• Authors: Ruyi Foong, Kai Keng Ang, Chai Quek, Cuntai Guan, Kok Soon Phua, Christopher Wee Keong Kuah, Vishwanath Arun Deshmukh, Lester Hon Lum Yam, Deshan Kumar Rajeswaran, Ning Tang, Effie Chew, Karen Sui Geok Chua
• Journal: IEEE transactions on bio-medical engineering (IEEE Trans Biomed Eng) Vol. 67 Issue 3 Pg. 786-795 (03 2020) ISSN: 1558-2531 [Electronic] United States
• PMID: 31180829 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Adult
• Aged
• Aged, 80 and over
• Brain-Computer Interfaces
• Electroencephalography (methods)
• Feedback, Sensory (physiology)
• Humans
• Imagination (physiology)
• Mental Fatigue (physiopathology)
• Middle Aged
• Motor Skills (physiology)
• Stroke Rehabilitation (methods)
• Upper Extremity (physiology)
• Young Adult