ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2014
Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 189 citations in OpenAlex.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.Journal of neuroengineering and rehabilitation · 2016Trial
- Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.Frontiers in computational neuroscience · 2024Review
- A method for synchronized use of EEG and eye tracking in fully immersive VR.Frontiers in human neuroscience · 2024Article
- A scoping review of gaze and eye tracking-based control methods for assistive robotic arms.Frontiers in robotics and AI · 2024Article
- A Novel Asynchronous Brain Signals-Based Driver-Vehicle Interface for Brain-Controlled Vehicles.Bioengineering (Basel, Switzerland) · 2023Article
- When neuro-robots go wrong: A review.Frontiers in neurorobotics · 2023Review
- The science and engineering behind sensitized brain-controlled bionic hands.Physiological reviews · 2022Review
- Continuous Decoding of Hand Movement From EEG Signals Using Phase-Based Connectivity Features.Frontiers in human neuroscience · 2022Article
- Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding.Frontiers in neurorobotics · 2022Article
- Controlling an effector with eye movements: The effect of entangled sensory and motor responsibilities.PloS one · 2022Article
- Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.Sensors (Basel, Switzerland) · 2021Review
- Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot.Sensors (Basel, Switzerland) · 2021Article
- Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature.Sensors (Basel, Switzerland) · 2021Review
- An artificial intelligence that increases simulated brain-computer interface performance.Journal of neural engineering · 2021Article
- Impact of Shared Control Modalities on Performance and Usability of Semi-autonomous Prostheses.Frontiers in neurorobotics · 2021Article
- Electrode-free visual prosthesis/exoskeleton control using augmented reality glasses in a first proof-of-technical-concept study.Scientific reports · 2020Article
- Non-contact visual control of personalized hand prostheses/exoskeletons by tracking using augmented reality glasses.3D printing in medicine · 2020Article
- A Hybrid Speller Design Using Eye Tracking and SSVEP Brain-Computer Interface.Sensors (Basel, Switzerland) · 2020Article
- The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions.Frontiers in neuroscience · 2020Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
Abstract
To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.
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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.