ArticleNature communications2025
EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Muscle Fiber-Based Soft Hand Exoskeleton Control Strategy for Fine Manipulation: A Preliminary Investigation.Biomimetics (Basel, Switzerland) · 2026Article
- From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG-EMG Fusion Brain-Computer Interfaces for Stroke Rehabilitation.Micromachines · 2026Review
- Review
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- Concurrent control of natural and robotic limbs through a tactile-encoded brain-computer interface.Nature communications · 2026Article
- Decoding motor imagery related to major mimetic muscles from electroencephalography.Journal of neuroengineering and rehabilitation · 2026Article
- Adaptive Neural Reorganization Enables Real-Time Finger-Level Robotic Control in BCI-Naïve Stroke Survivors.bioRxiv : the preprint server for biology · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Advances in neuroprostheses: interfaces, materials, and applications.Nano convergence · 2026Review
- Intention-Action Conflict EEG-Hand Kinematics Dataset for Unimanual Control under Congruent and Incongruent Conditions.Scientific data · 2026Article
- Brain responses to different action observation paradigms and assessing transferable cross-paradigm decoding.Journal of neuroengineering and rehabilitation · 2026Article
- 2025 Advances in Brain Health: Brain Health as Capital for Vascular and Cognitive Resilience.Stroke · 2026Article
- Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.Nano-micro letters · 2026Review
- Closed-loop brain-computer interfaces for post-stroke sensorimotor loop restoration.Frontiers in neuroscience · 2026Review
- Open access individual finger movement dataset with fNIRS.Frontiers in human neuroscience · 2026Article
- Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.Frontiers in neuroscience · 2026Article
- Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.Frontiers in rehabilitation sciences · 2026Review
- Decoding multi-joint hand movements from brain signals by learning a synergy-based neural manifold.Patterns (New York, N.Y.) · 2025Article
- Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review.Brain sciences · 2025Review
- High-Resolution EEG Source Reconstruction from PCA-Corrected BEM-FMM Reciprocal Basis Funcions: A Study with Visual Evoked Potentials from Intermittent Photic Stimulation.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level.
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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.