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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
Published on: July 14, 2023
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Changes in Electroencephalography Complexity using a Brain Computer Interface-Motor Observation Training in Chronic
Rui Sun1, Wan-Wa Wong1, Jing Wang1,2
1Division of Biomedical Engineering, Department of Electronic Engineering, Chinese University of Hong KongHong Kong, Hong Kong.
Frontiers in Human Neuroscience
|September 21, 2017
Summary
Electroencephalography (EEG) complexity, measured by fuzzy approximate entropy (fApEn), was lower in stroke patients. A brain-computer interface (BCI) intervention improved motor function and increased EEG fApEn, suggesting it can track recovery.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Rehabilitation Science
Background:
- Electroencephalography (EEG) complexity is a potential biomarker for neurological conditions.
- Chronic stroke often involves altered brain activity patterns.
- Brain-computer interface (BCI) interventions show promise for motor recovery.
Purpose of the Study:
- To investigate EEG complexity differences between chronic stroke subjects and unimpaired individuals.
- To evaluate the impact of a BCI-motor observation intervention on EEG complexity and motor function in stroke patients.
- To explore the relationship between EEG complexity changes and motor recovery.
Main Methods:
- Applied fuzzy approximate entropy (fApEn), an entropy-based algorithm, to EEG signals.
- Recruited 11 chronic stroke subjects and 9 unimpaired controls.
- Assessed motor function using Fugl-Meyer Assessment-Upper Limb (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT) before and after BCI training.
Main Results:
- Stroke subjects exhibited significantly lower EEG fApEn in motor cortex areas compared to controls (p < 0.05).
- BCI-motor observation intervention led to significant improvements in upper limb motor function (p < 0.05).
- EEG fApEn increased significantly in the contralesional hemisphere's central area post-intervention (p < 0.05), correlating with motor function improvements.
Conclusions:
- Fuzzy approximate entropy (fApEn) can identify abnormal EEG complexity in chronic stroke.
- Increased EEG fApEn post-BCI intervention may serve as an indicator of upper limb motor function recovery.
- Entropy-based EEG analysis offers a novel approach to understanding stroke-related cortical dynamics and rehabilitation effects.
