ReviewFrontiers in computational neuroscience2024
Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.
Review in Frontiers in computational neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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Who cites it
20 citing papers in PubMed.
- Closed-Loop Neuroprosthetics: Advancing Long-Term Solutions for Brain-Level Function Restoration.Cureus · 2026Review
- The classification of walking and phases of gait using EEG: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Rethinking Brain-Computer Interfaces for Soft Robotic Systems: A Unified Framework and Perspective.Sensors (Basel, Switzerland) · 2026Article
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Rewiring Attention: Virtual Reality and Brain-Computer Interfaces in the Rehabilitation of Unilateral Spatial Neglect.Journal of clinical medicine · 2026Review
- Stroke Rehabilitation, Novel Technology and the Internet of Medical Things.Brain sciences · 2026Review
- Article
- Rehab-DRLX: explainable neurorehabilitation prognosis using deep reinforcement learning and transformer-based models.Frontiers in computational neuroscience · 2026Article
- Closed-loop brain-computer interfaces for post-stroke sensorimotor loop restoration.Frontiers in neuroscience · 2026Review
- Sensorimotor Integration by Targeted Priming in Muscles with Electromyography-Driven Electro-vibro-feedback in Robot-Assisted Wrist/Hand Rehabilitation after Stroke.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- A theoretical framework for brain-computer interfaces: decodability, performance limits, and closed-loop adaptation.Frontiers in human neuroscience · 2026Article
- Decision-centered wearable biosensors for personalized rehabilitation: integrating multimodal monitoring, artificial intelligence, and closed-loop intervention.Frontiers in bioengineering and biotechnology · 2026Review
- Closed-loop neuromodulation: a paradigm shift in precision neuroscience for clinical and cognitive applications.Frontiers in neuroscience · 2026Review
- Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.Frontiers in rehabilitation sciences · 2026Review
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Editorial: The Application of EEG in Neurorehabilitation.Brain sciences · 2025Article
- Auditing cognitive drift in AI-driven recommendation: a responsible AI methods protocol with a health case demonstration.Frontiers in neuroscience · 2025Article
- Deep learning approaches for EEG-based healthcare applications: a comprehensive review.Frontiers in human neuroscience · 2025Review
- Advancements in the application of brain-computer interfaces based on different paradigms in amyotrophic lateral sclerosis.Frontiers in neuroscience · 2025Review
- Integrating music therapy and video games in cognitive interventions: innovative applications of closed-loop EEG.Frontiers in aging neuroscience · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.
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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.