A Review of Brain-Computer Interfaces in Neurorehabilitation and Nursing Care for Patients with Paraplegia

The application of brain–computer interfaces in spinal cord injury rehabilitation: Progress and prospects

With the continuous development of artificial intelligence, novel biomaterials, and immersive technologies such as virtual reality, BCIs are expected to evolve toward more personalized, home-based, and intelligent rehabilitation solutions, accelerating their clinical application and offering new therapeutic hope for SCI patients.

A Review of Medical Applications of Brain-Computer Interface Technology

The study stresses the necessity of embedding relational autonomy and neural rights into BCI development, tying technological trajectories to governance demands in order to shape responsible paths for future neurotechnologies.

[Emerging role of brain-computer interface in orthopedic rehabilitation and motor function restoration].

Emerging brain computer interface-based approaches in stroke rehabilitation: A review of current evidence and comparison with conventional physiotherapy

Background: Stroke is one of the leading causes of long-term disability worldwide and is increasingly being reported among young adults, resulting in substantial physical, psychological, and socioeconomic challenges. Although conventional physiotherapy plays a central role in stroke rehabilitation, many individuals with severe motor deficits do not achieve complete functional recovery. In recent years, Brain–Computer Interface (BCI) technology has emerged as a promising adjunct to rehabilitation by directly interpreting brain activity to facilitate movement, promote neuroplasticity, and enhance motor recovery through external assistive devices. Objective: To review the current evidences on the role of Brain-Computer Interface (BCI) approaches and comparison with conventional physiotherapy in stroke rehabilitation. Methods: A literature review was performed by searching electronic databases, including PubMed, Google Scholar, ScienceDirect, ProQuest, and Mendeley, for studies published between 2015 and 2025. The search was carried out using keywords related to Brain–Computer Interface (BCI), EEG-based BCI, stroke rehabilitation, neuroplasticity, and neurorehabilitation. Studies were screened according to predefined inclusion and exclusion criteria, and 10 relevant articles comprising systematic reviews, meta-analyses, randomized controlled trials, review articles, and case studies were included for critical appraisal and evidence synthesis. Results: The Reviewed studies consistently showed that non-invasive EEG-based Brain–Computer Interface (BCI), when used alongside conventional physiotherapy and other rehabilitation approaches such as functional electrical stimulation, robotic-assisted therapy, and virtual reality, was associated with improved upper-limb motor function, motor control, functional independence, and neuroplasticity in individuals with stroke. Several studies also reported that BCI enhanced communication abilities in patients with severe paralysis and locked-in syndrome. While invasive BCI systems offered greater signal accuracy, non-invasive EEG-based BCIs were considered safer, more practical, and better suited for routine clinical rehabilitation. Conclusion: Based on the reviewed evidences, Brain–Computer Interface (BCI) shows promise as an adjunct to conventional physiotherapy for improving stroke rehabilitation outcomes. Further high-quality studies are needed to establish standardized protocols and confirm its long-term clinical effectiveness.

Application of non-invasive electroencephalogram-based brain-computer interfaces in post-stroke hand function rehabilitation.

BACKGROUND Post-stroke hand dysfunction severely limits patients' independence, and conventional rehabilitation often fails those without active movement. Noninvasive EEG-based brain-computer interface (BCI) technology addresses this by creating a closed-loop feedback system rooted in Hebbian learning principles. This system decodes rhythmic signals from the sensorimotor cortex during imagined hand movements in real-time. The decoded intention is then translated into commands to drive exoskeletons, functional electrical stimulation (FES), or virtual reality (VR) devices, thereby moving the affected limb. This process strengthens or remodels damaged neural pathways, promoting motor recovery. METHODS This article systematically outlines the neurophysiological basis of EEG-BCI and three major rehabilitation paradigms: motor imagery with physical feedback, motor imagery with virtual/multisensory feedback, and the steady-state visual evoked potential (SSVEP)-driven paradigm. RESULTS Studies confirm these approaches can improve upper limb function, showing significant potential. However, widespread clinical use faces challenges like low signal-to-noise ratios, significant individual variability, and "BCI blindness." CONCLUSIONS Future work should focus on improving decoding algorithms, developing more user-friendly devices, deepening mechanistic understanding, and establishing standardized clinical assessments. This review aims to offer valuable guidance for subsequent research.

Brain-computer interface training for motor recovery after stroke.

Overall, the certainty of evidence was low to very low, downgraded primarily for these risk of bias concerns, severe imprecision (due to small sample sizes), and potential publication bias.