Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke

Cheng, Nicholas; Phua, Kok Soon; Lai, Hwa Sen; Tam, Pui Kit; Tang, Ka Yin; Cheng, Kai; Yeow, Chen-Hua; Ang, Kai Keng; Guan, Cuntai; Lim, Jeong Hoon

doi:10.1109/tbme.2020.2984003

Cited by 79 publications

(55 citation statements)

References 91 publications

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“…In the included studies, the performed BCI mental practices were different (Figure 4B). Nine studies instructed the BCI group to imagine the movement of the affected hand, [8][9][10]12,13,37,39,41,42 whereas 3 studies asked the BCI group to attempt moving the affected hand. 11,17,40 The effect size on motor function recovery was higher for the studies using the IM (Hedge's g = 1.21; P < .001) compared with those using the MI (Hedge's g = 0.55; P = .089).…”

Section: Versus Immentioning

confidence: 99%

“…13,39 The MIT-Manus robot was used in 2 studies, 9,10 and haptic knob, an assisted soft robotic glove, and orthosis (hand and arm) robot were used in 1 each. 11,37,42 One study provided only visual feedback to the patients. 12 Finally, the study conducted by Pichiorri et al 41 used a virtual hand to provide the BCI feedback to the patients.…”

Section: Type Of Bci Feedbackmentioning

confidence: 99%

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Efficacy of Brain–Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials

et al. 2021

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Background. A number of recent randomized controlled trials reported the efficacy of brain–computer interface (BCI) for upper-limb stroke rehabilitation compared with other therapies. Despite the encouraging results reported, there is a significant variance in the reported outcomes. This paper aims to investigate the effectiveness of different BCI designs on poststroke upper-limb rehabilitation. Methods. The effect sizes of pooled and individual studies were assessed by computing Hedge’s g values with a 95% confidence interval. Subgroup analyses were also performed to examine the impact of different BCI designs on the treatment effect. Results. The study included 12 clinical trials involving 298 patients. The analysis showed that the BCI yielded significant superior short-term and long-term efficacy in improving the upper-limb motor function compared to the control therapies (Hedge’s g = 0.73 and 0.33, respectively). Based on our subgroup analyses, the BCI studies that used the intention of movement had a higher effect size compared to those used motor imagery (Hedge’s g = 1.21 and 0.55, respectively). The BCI studies using band power features had a significantly higher effect size than those using filter bank common spatial patterns features (Hedge’s g = 1.25 and − 0.23, respectively). Finally, the studies that used functional electrical stimulation as the BCI feedback had the highest effect size compared to other devices (Hedge’s g = 1.2). Conclusion. This meta-analysis confirmed the effectiveness of BCI for upper-limb rehabilitation. Our findings support the use of band power features, the intention of movement, and the functional electrical stimulation in future BCI designs for poststroke upper-limb rehabilitation.

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Section: Versus Immentioning

confidence: 99%

Section: Type Of Bci Feedbackmentioning

confidence: 99%

Efficacy of Brain–Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials

et al. 2021

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“…This could be done by adapting the exoskeleton to the individual, which would be possible when the components are 3D-printed. Another option could be to use soft exoskeletons, such as a glove or sleeve, that can perform the intended movements [64].…”

Section: Limitations and Future Perspectivesmentioning

confidence: 99%

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Jochumsen

Janjua

Arceo

et al. 2021

Sensors

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Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

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“…Indeed, in addition to managing wheelchair movement and control, a MI BCI has a wide range of applications, such as virtual reality, neurorehabilitation, and controlling robotic devices [ 52 ]. The scientific research on BCI technology has also been focused on other medical applications [ 52 , 53 , 54 , 55 ], with many BCIs intended for the replacement or restoration of central nervous system (CNS) functionality lost due to illnesses (such as amyotrophic lateral sclerosis and locked-in syndrome) or to trauma (such as spinal cord injury), and others focused on therapy and motor rehabilitation [ 38 ].…”

Section: Brain–computer Interfaces Classifications and Applications: A Synthetic Overviewmentioning

confidence: 99%

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review

Palumbo

Gramigna

Calabrese

et al. 2021

Sensors

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The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.

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Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke

Cited by 79 publications

References 91 publications

Efficacy of Brain–Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Efficacy of Brain–Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review

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