ReviewSensors (Basel, Switzerland)2020
Brain-Computer Interface-Based Humanoid Control: A Review.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 91 citations in OpenAlex.
- Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials.Frontiers in neuroscience · 2022Pooled it
- Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Engineering signalling pathways in mammalian cells.Nature biomedical engineering · 2024Review
- Neural interfaces: Bridging the brain to the world beyond healthcare.Exploration (Beijing, China) · 2024Article
- The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.Cognitive neurodynamics · 2024Article
- Development of a humanoid robot control system based on AR-BCI and SLAM navigation.Cognitive neurodynamics · 2024Article
- On the role of generative artificial intelligence in the development of brain-computer interfaces.BMC biomedical engineering · 2024Review
- Identifying Thematics in a Brain-Computer Interface Research.Computational intelligence and neuroscience · 2023Review
- A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.Frontiers in neuroscience · 2023Review
- On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders.Frontiers in human neuroscience · 2023Review
- Locked-in syndrome revisited.Therapeutic advances in neurological disorders · 2023Review
- [Research advances in non-invasive brain-computer interface control strategies].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Sonomyography shows feasibility as a tool to quantify joint movement at the muscle level.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2022Article
- Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory.Sensors (Basel, Switzerland) · 2022Article
- Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.Sensors (Basel, Switzerland) · 2022Article
- Machine learning in biosignals processing for mental health: A narrative review.Frontiers in psychology · 2022Review
- A Human-Machine Interface Based on an EOG and a Gyroscope for Humanoid Robot Control and Its Application to Home Services.Journal of healthcare engineering · 2022Article
- Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds.Frontiers in systems neuroscience · 2022Article
- Patent landscape of brain-machine interface technology.Nature biotechnology · 2021Article
- Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces.APL bioengineering · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.
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.