ReviewFrontiers in rehabilitation sciences2026
Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.
Review in Frontiers in rehabilitation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative efficacy of virtual reality, robotics, and brain-computer interface interventions for upper limb rehabilitation after stroke: a systematic review and network meta-analysis.Frontiers in neurology · 2026Pooled it
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neurological disorders represent a significant category of diseases that profoundly affect human health, accounting for the second leading cause of global mortality. This group of conditions includes stroke, multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), spinal cord injury, Parkinson's disease, and cerebral palsy, among others. These disorders are highly susceptible to sequelae and profoundly impact individuals' daily lives. In this context, Brain-Computer Interface (BCI) technology has demonstrated considerable potential in the domain of neurorehabilitation, although numerous challenges remain. The manuscript provides a comprehensive review of recent advancements in research and clinical applications, highlighting current limitations and outlining future directions. It elucidates the applicability and constraints of Brain-Computer Interface (BCI) technology across various diseases and patient populations. To facilitate insights across different conditions, comparative tables are presented, aligning BCI strategies with therapeutic targets, outcomes, advantages, limitations, and existing evidence gaps. The scope extends beyond motor restoration to include under-explored domains, such as neuropathic pain, with a focus on real-world translation, including home and community feasibility and the distinction between assistive and rehabilitative applications. The review distills overarching limitations within the field, such as small sample sizes, protocol heterogeneity, and limited longitudinal evidence, while synthesizing the most recent studies. An actionable research and development roadmap is proposed to guide next-generation BCI rehabilitation, incorporating individualized cortical-network simulators, self-architecting decoders, adaptive therapy approaches akin to game seasons, and proprioceptive "write-back" mechanisms via peripheral interfaces. Moreover, the review reveals significant research focal points and critical issues that warrant further investigation in the context of neurological rehabilitation utilizing BCI technology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.