review · Applied Sciences
Brain-computer interface systems combined with electroencephalogram devices play a crucial role in rehabilitating individuals with impaired muscles and motor functions. Recent advancements demonstrate the value of coupling neural monitoring with machine learning and deep learning algorithms to evaluate and assist motor recovery. Key developments focus on accessible technological aids and novel robotic prosthetics controlled directly by brain activity. The literature underscores the adaptability of prevalent electroencephalogram hardware for therapeutic interventions, alongside evidence from case studies demonstrating practical implementations across diverse patient cohorts. These combined approaches improve motor control, enhance robotic integration, and reshape existing rehabilitation practices. Overall, synthesising current methodologies provides a foundation for informed technological selection and guides future directions in brain-computer interface research for motor system recovery.
Motor impairments caused by conditions such as stroke present major challenges for long-term recovery. Harnessing brain signals to operate assistive technology and robotic prosthetics introduces more direct, responsive therapeutic interventions. Identifying suitable brain-monitoring hardware and machine learning tools helps researchers and healthcare providers develop practical systems that restore physical movement and improve quality of life.
This work informs developers of medical devices, assistive robotics companies, and clinical rehabilitation centres seeking to deploy brain-computer interface systems. Key applications include brain-controlled robotic prosthetics and algorithm-based rehabilitation assessment tools. Given the existence of adaptable commercial electroencephalogram devices and successful real-world case studies, the sector demonstrates applied and tested implementations, though continuous development is required to standardise algorithms for broader clinical adoption.
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This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly technological support and robotic prosthetics powered by brain activity. This review critically examines the latest strides in BCI technology and its application in motor skill recovery. Special attention is given to prevalent EEG devices adaptable for BCI-driven rehabilitation. The study surveys significant contributions in the realm of machine learning-based and deep learning-based rehabilitation evaluation. The integration of BCI with EEG technology demonstrates promising outcomes for enhancing motor skills in rehabilitation. The study identifies key EEG devices suitable for BCI applications, discusses advancements in machine learning approaches for rehabilitation assessment, and highlights the emergence of novel robotic prosthetics powered by brain activity. Furthermore, it showcases successful case studies illustrating the practical implementation of BCI-driven rehabilitation techniques and their positive impact on diverse patient populations. This review serves as a cornerstone for informed decision-making in the field of BCI technology for rehabilitation. The results highlight BCI’s diverse advantages, enhancing motor control and robotic integration. The findings highlight the potential of BCI in reshaping rehabilitation practices and offer insights and recommendations for future research directions. This study contributes significantly to the ongoing transformation of BCI technology, particularly through the utilization of EEG equipment, providing a roadmap for researchers in this dynamic domain.
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DOI: 10.3390/app14146347
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