Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application

Khan, Muhammad Ahmed; Das, Rig; Iversen, Helle K.; Puthusserypady, Sadasivan

doi:10.1016/j.compbiomed.2020.103843

Cited by 171 publications

(114 citation statements)

References 130 publications

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“…The effector of a BCI can take multiple forms. For neurorehabilitation, it may be a device that assists the patient to complete movements, such as a robotic limb (Tariq et al, 2018;Soekadar et al, 2019;, a device that gives virtual (e.g., on-screen) feedback to the participant to promote appropriate patterns of neural activity (Kerous et al, 2018;Si-Mohammed et al, 2018;de Castro-Cros et al, 2020), or a trigger to induce electrical stimulation of muscles in order to evoke movement (Biasiucci et al, 2018;Bai et al, 2020). Even in the absence of evoked movement, electrical stimulation can be used below motor threshold to provide continuous somatosensory feedback as the BCI signal (Corbet et al, 2018).…”

Section: Brain-computer Interface For Neurorehabilitation; Basic Premise and Scope Of The Reviewmentioning

confidence: 99%

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

et al. 2021

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Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.

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Section: Brain-computer Interface For Neurorehabilitation; Basic Premise and Scope Of The Reviewmentioning

confidence: 99%

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

et al. 2021

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“…The accuracy of the cortical signals obtained by noninvasive BCI systems is not as high as the signals from invasive BCI [ 40 ], but portability, safety, comfort, and low cost make noninvasive BCI the first choice for obtaining relevant brain electrical signals and electroencephalogram (EGG) [ 41 ]. These devices include wireless EEG which offers reduced noise and signal artifacts that can be generated by the movement of wired EEG devices [ 42 ].…”

Section: Brain-computer Interface (Bci)mentioning

confidence: 99%

Discussion on the Rehabilitation of Stroke Hemiplegia Based on Interdisciplinary Combination of Medicine and Engineering

Sun

Shi

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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Interdisciplinary combinations of medicine and engineering are part of the strategic plan of many universities aiming to be world-class institutions. One area in which these interactions have been prominent is rehabilitation of stroke hemiplegia. This article reviews advances in the last five years of stroke hemiplegia rehabilitation via interdisciplinary combination of medicine and engineering. Examples of these technologies include VR, RT, mHealth, BCI, tDCS, rTMS, and TCM rehabilitation. In this article, we will summarize the latest research in these areas and discuss the advantages and disadvantages of each to examine the frontiers of interdisciplinary medicine and engineering advances.

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“…Those experiments paved the way for the development of non-invasive BCI paradigms that made use of neuroimaging techniques as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) (see Rao, 2013 for a comprehensive review). Indeed, by translating the recorded neural activity into digital commands via mathematical and AI methods (see Wolpaw et al, 2002) (Figure 1), BCI enables controlling external devices with the brain (e.g., Padfield et al, 2019;Khan et al, 2020), such as a computer, a robot, or an exoskeleton (e.g., Nuyujukian et al, 2018;Benabid et al, 2019;Moses et al, 2019). This ability is particularly interesting in specific contexts where voice or motor commands cannot be used (e.g., Lin et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

Douibi

Bars

Lemontey

et al. 2021

Front. Hum. Neurosci.

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In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.

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Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application

Cited by 171 publications

References 130 publications

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

Discussion on the Rehabilitation of Stroke Hemiplegia Based on Interdisciplinary Combination of Medicine and Engineering

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

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