Neural mechanisms underlying the improvement of gait disturbances in stroke patients through robot-assisted gait training based on QEEG and fNIRS: a randomized controlled study

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Neural mechanisms underlying the improvement of gait disturbances in stroke patients through robot-assisted gait training based on QEEG and fNIRS: a randomized controlled study

• Xiang Li, 
• Huihuang Zhang, 
• Wanying Zhang, 
• Jianing Wu, 
• Lei Dai, 
• Nasha Long, 
• Tiefeng Jin, 
• Lei Gu & 
• Jianer Chen 

Journal of NeuroEngineering and Rehabilitation volume 22, Article number: 136 (2025) Cite this article

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Abstract

Background

Robot-assisted gait training is more effective in improving lower limb function and walking ability in stroke patients compared to conventional rehabilitation, but the neural mechanisms remain unclear. This study aims to explore the effects of robot-assisted gait training on lower limb motor dysfunction in stroke patients and its impact on neural activity in the motor cortex, providing objective evidence for clinical application.

Methods

Forty-two stroke patients meeting the inclusion criteria were randomly assigned to either the experimental group receiving robot-assisted gait training or the control group receiving conventional overground walking training. Assessments were conducted at baseline and after four weeks of treatment. Primary outcome measures included cortical activation measured by functional near-infrared spectroscopy (fNIRS), power ratio index (PRI), and delta/alpha power ratio (DAR) measured by quantitative electroencephalography (QEEG), and their correlation with the Fugl-Meyer Assessment (FMA) for lower limb motor function. Secondary outcome measures included FMA and Functional Ambulation Category (FAC).

Results

Data from 36 patients (18 in each group) after four weeks of treatment were analyzed. The fNIRS results indicated better activation in the premotor and supplementary motor cortices in the robot-assisted gait training group compared to the control group. QEEG analysis showed reduced PRI and DAR in the premotor, supplementary motor, and primary motor cortices in the robot-assisted gait training group, suggesting improved motor function recovery in stroke patients. Clinical scale analysis revealed superior motor function recovery in the robot-assisted gait training group compared to the control group.

Conclusions

Robot-assisted gait training significantly enhances activation in the primary motor cortex and supplementary motor area, potentially aiding stroke patients in recovering their ability to plan. PRI and DAR, particularly PRI, are valuable clinical indicators for assessing motor function recovery in stroke patients.

Trial registration

Chinese Clinical Trial Registry (ChiCTR2200060668). Registered on June 6, 2022; https://www.chictr.org.cn/showproj.html?proj=171610.

Background

Stroke is a global health issue and one of the leading causes of long-term disability. Approximately one-third of stroke patients experience permanent motor deficits, severely affecting their daily activities [1]. Lower limb motor dysfunction is a common problem among stroke patients, leading to difficulties in mobility, posture maintenance, balance, and walking. Therefore, providing rehabilitation to improve walking ability in stroke patients is necessary [2, 3].

In recent years, rehabilitation robots have become increasingly important in clinical rehabilitation [4]. Their application can relieve therapists from strenuous training tasks. By analyzing data from rehabilitation robot training, the patient’s recovery status can be assessed [5]. Due to their precision and reliability, rehabilitation robots are an effective method for improving stroke rehabilitation [6].

Currently, the neurophysiological mechanisms by which rehabilitation robots enhance functional walking ability remain unclear [7, 8]. Some scholars believe that the effectiveness of rehabilitation robots in improving functional walking ability depends on the high repetition frequency and intensity of task-oriented movements [9]. Studies have shown that conventional exercise therapy can enhance patients’ neuroplasticity [10, 11]. Compared to traditional therapy, robot-assisted gait training may more effectively promote neuroplasticity mechanisms related to motor learning and functional recovery, such as sensorimotor plasticity, effective connectivity of the frontal-parietal cortex, and interhemispheric inhibition [12].

The rise of multimodal neuroimaging technologies has significantly impacted modern neuroscience. These methods contribute independently to understanding cognitive processing [13, 14] and improving clinical diagnosis [15]. Functional near-infrared spectroscopy (fNIRS) combined with quantitative electroencephalography (QEEG) is currently favored due to its non-invasiveness, low cost, and system flexibility [16]. fNIRS is suitable for monitoring cortical activation during dynamic movement, making it possible to visualize cortical activation during dynamic movement [17]. Based on this, this research will use fNIRS to detect patients before and after robot-assisted gait training, indirectly assessing cortical neural activation by observing changes in beta values across different brain regions.

QEEG can record synchronous postsynaptic potentials of cortical neurons from the scalp [18]. The raw electroencephalography (EEG) signal is amplified, digitized, mapped, and filtered to isolate narrow frequency bands (in Hz) reflecting specific brain sources and functions, typically divided into delta (0.3–3.5 Hz), theta (4–7.5 Hz), alpha (8–13 Hz), and beta (14–30 Hz) bands. This study will use the delta/alpha ratio (DAR) and the power ratio index (PRI), which is (delta + theta)/(alpha + beta), to assess the degree of motor dysfunction and motor gain in stroke patients.

Robot-assisted gait training has been shown to effectively improve walking ability, correct abnormal gait, and promote motor function recovery and balance in hemiplegic stroke patients, but its neural mechanisms remain unclear. In this study, hemiplegic stroke patients will undergo fNIRS and QEEG assessments over a four-week period both before and after receiving robot-assisted gait training and conventional gait training, with subsequent analysis of the correlations between EEG indices and clinical outcome measures. For the first time, this research combine fNIRS and QEEG to evaluate the dynamic effects of lower limb robotic rehabilitation on the motor cortex, providing multidimensional evidence to elucidate the neuroplastic mechanisms underlying lower limb robotic therapy in stroke patients and laying a theoretical foundation for the design of personalized rehabilitation protocols.

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