ArticleBiomedical engineering online2015
A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.
Article in Biomedical engineering online, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine Learning- and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and EMG Signals: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control.Frontiers in bioengineering and biotechnology · 2025Article
- A Case Study on EEG Signal Correlation Towards Potential Epileptic Foci Triangulation.Sensors (Basel, Switzerland) · 2024Article
- Cybernic robot hand-arm that realizes cooperative work as a new hand-arm for people with a single upper-limb dysfunction.Frontiers in robotics and AI · 2024Article
- A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces.Scientific data · 2023Article
- Article
- Vision-aided brain-machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury.Biomedical engineering online · 2019Article
- Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze-Brain Machine Interface.Frontiers in neurorobotics · 2019Article
- Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback.Frontiers in neurorobotics · 2017Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundA brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although invasive neural signals have a high spatial resolution, non-invasive neural signals are valuable because they provide an interface without surgery. Thus, various researchers have developed robot arms driven by non-invasive neural signals. However, robot arm control based on the imagined trajectory of a human hand can be more intuitive for patients. In this study, therefore, an integrated robot arm-gripper system (IRAGS) that is driven by three-dimensional (3D) hand trajectories predicted from non-invasive neural signals was developed and verified.
methodsThe IRAGS was developed by integrating a six-degree of freedom robot arm and adaptive robot gripper. The system was used to perform reaching and grasping motions for verification. The non-invasive neural signals, magnetoencephalography (MEG) and electroencephalography (EEG), were obtained to control the system. The 3D trajectories were predicted by multiple linear regressions. A target sphere was placed at the terminal point of the real trajectories, and the system was commanded to grasp the target at the terminal point of the predicted trajectories.
resultsThe average correlation coefficient between the predicted and real trajectories in the MEG case was [Formula: see text] ([Formula: see text]). In the EEG case, it was [Formula: see text] ([Formula: see text]). The success rates in grasping the target plastic sphere were 18.75 and 7.50 % with MEG and EEG, respectively. The success rates of touching the target were 52.50 and 58.75 % respectively.
conclusionsA robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because the non-invasive neural signal is less accurate. However the success rate could be sufficiently improved for practical applications by using additional sensors. Robot arm control based on hand trajectories predicted from EEG would allow for portability, and the performance with EEG was comparable to that with MEG.
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