Neurofeedback in the Rehabilitation of Patients with Motor Disorders after Stroke

Kovyazina, M.; Varako, N.; Lyukmanov, Roman; Asiatskaya, G. A.; Супонева, Н. А.; Trofimova, Alexandra

doi:10.1134/s0362119719040042

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…As such, they can engage in mental practise of movement and keep their motor neural circuitry active, warding off the detrimental effects of limb non-use (Buxbaum et al, 2020), its associated white matter degeneration (Egorova et al, 2020), and promote usedependant neuroplastic processes (Xing and Bai, 2020). Despite largely encouraging evidence suggesting that functional gains are produced exceeding those of standard care (Hatem et al, 2016;McConnell et al, 2017;Monge-Pereira et al, 2017;Cervera et al, 2018;López-Larraz et al, 2018;Raffin and Hummel, 2018;Carvalho et al, 2019;Coscia et al, 2019;Kovyazina et al, 2019;Remsik et al, 2016;Xing and Bai, 2020) substantial challenges with existing BCI implementations have prevented widespread adoption of the technology clinically (Baniqued et al, 2021). Here we provide a viewpoint on the practical, technical and mechanistic factors impeding the scalability of BCI into rehabilitative care packages.…”

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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“…One example of a game that utilizes BCI is MindRDR, developed by the Londonbased startup This Place [143]. The game uses EEG sensors to measure players' emotional responses while playing.…”

Section: Gamingmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

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“…Numerous studies have shown that BCI therapy can improve the motor function of the upper extremities of subacute and chronic stroke patients. [ 11 12 13 14 15 16 17 18 ] MI-BCI along with conventional physical practice has shown significant improvement compared to physical practice alone. [ 7 ] Ipsilesional motor cortices activation and effective bilateral motor cortices connectivity are thought to play a major role in motor recovery.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Event Related Desynchronization in Chronic Stroke Using Motor Imagery Based Brain Computer Interface for Upper Limb Rehabilitation

Gangadharan,

Ramakrishnan,

Paek

et al. 2024

Annals of Indian Academy of Neurology

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Objective: Motor imagery-based brain–computer interface (MI-BCI) is a promising novel mode of stroke rehabilitation. The current study aims to investigate the feasibility of MI-BCI in upper limb rehabilitation of chronic stroke survivors and also to study the early event-related desynchronization after MI-BCI intervention. Methods: Changes in the characteristics of sensorimotor rhythm modulations in response to a short brain–computer interface (BCI) intervention for upper limb rehabilitation of stroke-disabled hand and normal hand were examined. The participants were trained to modulate their brain rhythms through motor imagery or execution during calibration, and they played a virtual marble game during the feedback session, where the movement of the marble was controlled by their sensorimotor rhythm. Results: Ipsilesional and contralesional activities were observed in the brain during the upper limb rehabilitation using BCI intervention. All the participants were able to successfully control the position of the virtual marble using their sensorimotor rhythm. Conclusions: The preliminary results support the feasibility of BCI in upper limb rehabilitation and unveil the capability of MI-BCI as a promising medical intervention. This study provides a strong platform for clinicians to build upon new strategies for stroke rehabilitation by integrating MI-BCI with various therapeutic options to induce neural plasticity and recovery.

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Neurofeedback in the Rehabilitation of Patients with Motor Disorders after Stroke

Cited by 5 publications

References 48 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

State-of-the-Art on Brain-Computer Interface Technology

Characterization of Event Related Desynchronization in Chronic Stroke Using Motor Imagery Based Brain Computer Interface for Upper Limb Rehabilitation

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