ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2015
Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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
74 citing papers in PubMed, 1 synthesis or guideline pooled it, 232 citations in OpenAlex.
- Brain-computer interface for post-stroke upper-limb recovery: a systematic review integrating clinical efficacy with neuroplasticity evidence.Frontiers in neurology · 2026Pooled it
- Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients.Journal of neuroengineering and rehabilitation · 2024Trial
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- The evolution of speech communication devices for anarthria: a review.Journal of neurology · 2026Review
- Investigating the neurovascular coupling across multiple motor execution and imagery conditions: a whole-brain EEG-informed fMRI analysis.NeuroImage · 2025Article
- A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification.Biomimetics (Basel, Switzerland) · 2025Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- Continuous Reaching and Grasping With a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Adaptive deep feature representation learning for cross-subject EEG decoding.BMC bioinformatics · 2024Article
- A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding.Cognitive neurodynamics · 2024Article
- A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.Scientific data · 2024Article
- Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs.Scientific reports · 2024Article
- Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.Sensors (Basel, Switzerland) · 2024Article
- Time-frequency feature extraction based on multivariable synchronization index for training-free SSVEP-based BCI.Cognitive neurodynamics · 2024Article
- Three-stage transfer learning for motor imagery EEG recognition.Medical & biological engineering & computing · 2024Article
- EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels.Brain informatics · 2024Article
- Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.PNAS nexus · 2024Article
- Multi-information improves the performance of CCA-based SSVEP classification.Cognitive neurodynamics · 2024Article
- Integrating Simultaneous Motor Imagery and Spatial Attention for EEG-BCI Control.IEEE transactions on bio-medical engineering · 2024Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.
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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.