Effects of Training with a Brain–Computer Interface-Controlled Robot on Rehabilitation Outcome in Patients with Subacute Stroke: A Randomized Controlled Trial

Zhao, Chenguang; Ju, Fengkui; Sun, Wei; Jiang, Shan; Xiao, Xi; Wang, Wanjun; Sun, Xiaolong; Li, Min; Xie, Jun; Zhang, Kai; Xu, Guanghua; Sicong, Zhang; Mou, Xiang; Yuan, Hua

doi:10.1007/s40120-022-00333-z

Cited by 16 publications

(11 citation statements)

References 51 publications

(58 reference statements)

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“…Most of the included studies didn't report any adverse effect during trials. Ten of the studies reported that no serious adverse effects were found in the participants during and after BCI training or intervention s [13,14,24,28,31,38,42,45,50,52].…”

Section: Adverse Effectsmentioning

confidence: 99%

Theraputic Effects of Brain-Computer Interface on Motor Recovery of Stroke Patients: A Meta-analysis

Gao

Jiang

et al. 2023

Preprint

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Purpose<break>The aim of this study was to evaluate the therapeutic effects of brain-computer interface (BCI) on improving motor functions of stroke patients.<break>Methods<break>We conducted a meta-analysis on randomized controlled trials (RCTs) on BCI training for post-stroke motor rehabilitation. Relevant publications were identified from the databases of PubMed, Embase, ScienceDirect, and Cochrane Library. The standardized mean difference (SMD) with 95% confidence intervals (CI) were calculated as the pooled effect size of the motor outcome.<break>Results<break>Thirty-five of the 43 candidate articles involving 749 participants were included in this meta-analysis. Overall, both the significant immediate effect size of 0.53 and long-lasting effect size of 0.26 were found for motor outcome measured by Fugl-Meyer Assessment. A further subgroup-analysis observed larger therapeutic effects on lower-limb than upper-limb. A subgroup-analysis also indicated that stroke patients may gain better functional outcome in the subacute phase than in the chronic phase. Superior effect of BCI training was also detected for distal function of upper-limb over proximal function. BCI training combined with functional electrical stimulation (FES) was more effective than BCI combined with robot . No significant effect was found in other combined interventional methods, especially the use of transcranial direct current stimulation, which cannot potentiate the effects of BCI training. In addition, subgroup-analysis also indicated a greater effect for longer durations of intervention. And the dosage between 15 min and 180 min was found to be optimal.<break>Conclusions<break>BCI has significant immediate and long-lasting effects on improving motor function of both upper-limb and lower-limb of stroke patients. Superior therapeutic could be delivered to patients in the subacute phase and clearer benefits are evident in distal functions of upper extremity. When combined with FES, BCI seems to be more effective than when combined with robot and other external devices. Longer durations of intervention could provide better effects. But bigger is not always better for weekly dosage.

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Section: Adverse Effectsmentioning

confidence: 99%

Theraputic Effects of Brain-Computer Interface on Motor Recovery of Stroke Patients: A Meta-analysis

Gao

Jiang

et al. 2023

Preprint

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“…There is no best recommendation for CPC therapy. The combination therapy schemes used in animal experiments and clinical trials can be divided into the following categories: central intervention combined with conventional rehabilitation therapy, NMES, TOT, use of a rehabilitation robot, or acupuncture (Li et al, 2018;Xu et al, 2018;Zhao et al, 2022). These findings provide the evidence and potential of the CPC closed-loop theory.…”

Section: Application Of Central-peripheral-central Closed-loop Rehabi...mentioning

confidence: 99%

Exploration on neurobiological mechanisms of the central–peripheral–central closed-loop rehabilitation

Jia

2022

Front. Cell. Neurosci.

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Central and peripheral interventions for brain injury rehabilitation have been widely employed. However, as patients’ requirements and expectations for stroke rehabilitation have gradually increased, the limitations of simple central intervention or peripheral intervention in the rehabilitation application of stroke patients’ function have gradually emerged. Studies have suggested that central intervention promotes the activation of functional brain regions and improves neural plasticity, whereas peripheral intervention enhances the positive feedback and input of sensory and motor control modes to the central nervous system, thereby promoting the remodeling of brain function. Based on the model of a central–peripheral–central (CPC) closed loop, the integration of center and peripheral interventions was effectively completed to form “closed-loop” information feedback, which could be applied to specific brain areas or function-related brain regions of patients. Notably, the closed loop can also be extended to central and peripheral immune systems as well as central and peripheral organs such as the brain–gut axis and lung–brain axis. In this review article, the model of CPC closed-loop rehabilitation and the potential neuroimmunological mechanisms of a closed-loop approach will be discussed. Further, we highlight critical questions about the neuroimmunological aspects of the closed-loop technique that merit future research attention.

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“…It is an actively evoked EEG signal that has high application values in the field of neurorehabilitation because it can independently elicit potential activity in motorrelated brain regions without external stimulation (Pfurtscheller and Neuper, 2001). The MI-BCI detects the user's motor intentions by capturing the potential changes, and the output command could be used to control functional electrical stimulation (FES), exoskeletons, or other rehabilitation assistive equipment (Biasiucci et al, 2018;Zhao et al, 2022). Thus MI-BCI is valuable in the medical rehabilitation pathway for patients with motor dysfunction through the provision of active rehabilitation training (Jeong et al, 2019;Romero-Laiseca et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network

Zhang

Chen

et al. 2023

Front. Neurosci.

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Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.

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Effects of Training with a Brain–Computer Interface-Controlled Robot on Rehabilitation Outcome in Patients with Subacute Stroke: A Randomized Controlled Trial

Cited by 16 publications

References 51 publications

Theraputic Effects of Brain-Computer Interface on Motor Recovery of Stroke Patients: A Meta-analysis

Theraputic Effects of Brain-Computer Interface on Motor Recovery of Stroke Patients: A Meta-analysis

Exploration on neurobiological mechanisms of the central–peripheral–central closed-loop rehabilitation

Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network

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