ArticleFrontiers in neurorobotics2017
Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback.
Article in Frontiers in neurorobotics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Multimodal brain-computer interface for robotic control: integration of real-time gaze tracking and EEG-based motor imagery.Medical & biological engineering & computing · 2026Article
- A scoping review of gaze and eye tracking-based control methods for assistive robotic arms.Frontiers in robotics and AI · 2024Article
- Design of auditory P300-based brain-computer interfaces with a single auditory channel and no visual support.Cognitive neurodynamics · 2023Article
- Metaverse Wearables for Immersive Digital Healthcare: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study.JMIR formative research · 2022Article
- Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future.Computational intelligence and neuroscience · 2022Review
- P300 Brain-Computer Interface-Based Drone Control in Virtual and Augmented Reality.Sensors (Basel, Switzerland) · 2021Article
- [Robotic arm control system based on augmented reality brain-computer interface and computer vision].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance.Frontiers in neuroscience · 2021Article
- The Advantage of Low-Delta Electroencephalogram Phase Feature for Reconstructing the Center-Out Reaching Hand Movements.Frontiers in neuroscience · 2019Article
- Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze-Brain Machine Interface.Frontiers in neurorobotics · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes.
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