scholarly journals Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

2019 ◽  
Vol 4 (31) ◽  
pp. eaaw6844 ◽  
Author(s):  
B. J. Edelman ◽  
J. Meng ◽  
D. Suma ◽  
C. Zurn ◽  
E. Nagarajan ◽  
...  
Keyword(s):  
Neural Control ◽  
Robotic Arm ◽  
User Engagement ◽  
Robotic Device ◽  
Real Time Control ◽  
Source Imaging ◽  
Home Setting ◽  
Time Control ◽  
Eeg Source Imaging ◽  
Noninvasive Neuroimaging

Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly improve the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework using electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the “brain” and “computer” components by increasing, respectively, user engagement through a continuous pursuit task and associated training paradigm and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by more than 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Last, this framework was deployed in a physical task, demonstrating a near-seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications for the eventual development and implementation of neurorobotics by means of noninvasive BCI.

scholarly journals EEG Source Imaging Guided by Spatiotemporal Specific fMRI: Toward an Understanding of Dynamic Cognitive Processes

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Thinh Nguyen ◽  
Thomas Potter ◽  
Trac Nguyen ◽  
Christof Karmonik ◽  
Robert Grossman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cognitive Rehabilitation ◽  
Motor System ◽  
Activity Patterns ◽  
Imaging Method ◽  
Source Imaging ◽  
Localization Accuracy ◽  
Motor Activation ◽  
Eeg Source Imaging ◽  
Noninvasive Neuroimaging

Understanding the mechanism of neuroplasticity is the first step in treating neuromuscular system impairments with cognitive rehabilitation approaches. To characterize the dynamics of the neural networks and the underlying neuroplasticity of the central motor system, neuroimaging tools with high spatial and temporal accuracy are desirable. EEG and fMRI stand among the most popular noninvasive neuroimaging modalities with complementary features, yet achieving both high spatial and temporal accuracy remains a challenge. A novel multimodal EEG/fMRI integration method was developed in this study to achieve high spatiotemporal accuracy by employing the most probable fMRI spatial subsets to guide EEG source localization in a time-variant fashion. In comparison with the traditional fMRI constrained EEG source imaging method in a visual/motor activation task study, the proposed method demonstrated superior localization accuracy with lower variation and identified neural activity patterns that agreed well with previous studies. This spatiotemporal fMRI constrained source imaging method was then implemented in a “sequential multievent-related potential” paradigm where motor activation is evoked by emotion-related visual stimuli. Results demonstrate that the proposed method can be used as a powerful neuroimaging tool to unveil the dynamics and neural networks associated with the central motor system, providing insights into neuroplasticity modulation mechanism.

Impact Analysis of the Memristor Failure on Real-Time Control System of Robotic Arm

2018 ◽  
Vol 49 (3) ◽  
pp. 1321-1333
Author(s):  
Jun Liu ◽  
Tianshu Li ◽  
Shukai Duan ◽  
Lidan Wang
Keyword(s):  
Control System ◽  
Real Time ◽  
Impact Analysis ◽  
Robotic Arm ◽  
Real Time Control ◽  
Time Control

scholarly journals Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Benzhen Guo ◽  
Yanli Ma ◽  
Jingjing Yang ◽  
Zhihui Wang ◽  
Xiao Zhang
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Robotic Arm ◽  
Myoelectric Signal ◽  
Real Time Control ◽  
Myoelectric Signals ◽  
Upper Limb Rehabilitation ◽  
Time Control ◽  
Offline Testing ◽  
Limb Rehabilitation

Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation ( p  = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

scholarly journals Dynamics Modeling of a Continuum Robotic Arm with a Contact Point in Planar Grasp

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Mohammad Dehghani ◽  
S. Ali A. Moosavian
Keyword(s):  
Degrees Of Freedom ◽  
Contact Point ◽  
Nonlinear Behavior ◽  
Robotic Arm ◽  
Dynamics Modeling ◽  
Real Time Control ◽  
Time Control ◽  
Cosserat Rod ◽  
Circular Arcs ◽  
Continuum Robotic

Grasping objects by continuum arms or fingers is a new field of interest in robotics. Continuum manipulators have the advantages of high adaptation and compatibility with respect to the object shape. However, due to their extremely nonlinear behavior and infinite degrees of freedom, continuum arms cannot be easily modeled. In fact, dynamics modeling of continuum robotic manipulators is state-of-the-art. Using the exact modeling approaches, such as theory of Cosserat rod, the resulting models are either too much time-taking for computation or numerically unstable. Thus, such models are not suitable for applications such as real-time control. However, based on realistic assumptions and using some approximations, these systems can be modeled with reasonable computational efforts. In this paper, a planar continuum robotic arm is modeled, considering its backbone as two circular arcs. In order to simulate finger grasping, the continuum arm experiences a point-force along its body. Finally, the results are validated using obtained experimental data.

scholarly journals Real-Time Control System for Improved Precision and Throughput in an Ultrafast Carbon Fiber Placement Robot Using a SoC FPGA Extended Processing Platform

2017 ◽  
Vol 2017 ◽  
pp. 1-20
Author(s):  
Gilberto Ochoa-Ruiz ◽  
Romain Bevan ◽  
Florent de Lamotte ◽  
Jean-Philippe Diguet ◽  
Cheng-Cong Bao
Keyword(s):  
Real Time ◽  
Robotic Arm ◽  
Real Time Control ◽  
Maximum Throughput ◽  
Original Solution ◽  
Real Time Operating System ◽  
Fiber Placement ◽  
Time Control ◽  
Retrieval Mechanism ◽  
Processing Platform

We present an architecture for accelerating the processing and execution of control commands in an ultrafast fiber placement robot. The system consists of a robotic arm designed by Coriolis Composites whose purpose is to move along a surface, on which composite fibers are deposed, via an independently controlled head. In first system implementation, the control commands were sent via Profibus by a PLC, limiting the reaction time and thus the precision of the fiber placement and the maximum throughput. Therefore, a custom real-time solution was imperative in order to ameliorate the performance and to meet the stringent requirements of the target industry (avionics, aeronautical systems). The solution presented in this paper is based on the use of a SoC FPGA processing platform running a real-time operating system (FreeRTOS), which has enabled an improved comamnd retrieval mechanism. The system’s placement precision was improved by a factor of 20 (from 1 mm to 0.05 mm), while the maximum achievable throughput was 1 m/s, compared to the average 30 cm/s provided by the original solution, enabling fabricating more complex and larger pieces in a significant fraction of the time.

Real-time control of a robotic arm by neuronal ensembles

10.1038/10131 ◽  
1999 ◽  
Vol 2 (7) ◽  
pp. 583-584 ◽  
Author(s):  
Eberhard E. Fetz
Keyword(s):  
Real Time ◽  
Robotic Arm ◽  
Real Time Control ◽  
Neuronal Ensembles ◽  
Time Control

scholarly journals Robotic Arm Control System Based on Brain-Muscle Mixed Signals

2021 ◽  
Author(s):  
Li-Wei Cheng ◽  
Duan-Ling Li ◽  
Gong-Jing Yu ◽  
Zhong-Hai Zhang ◽  
Shu-Yue Yu
Keyword(s):  
Control System ◽  
Real Time ◽  
Feature Recognition ◽  
Robotic Arm ◽  
Real Time Control ◽  
Existing Problems ◽  
Time Control ◽  
Emg Signal ◽  
Extended Application ◽  
Robotic Arm Control

Abstract Aiming at the existing problems of BCI (brain computer interface), such as single input signal source, low accuracy of feature recognition, and less output control instructions, this paper proposes a robotic arm control system based on EEG (electroencephalogram) and EMG (electromyogram) mixed signals. The system flow is as follows: Firstly, the EMG signal of the unilateral arm and the EEG signal of the left and right hand motor imagery is collected synchronously. Then the collected EEG and EMG signals are extracted and classified, and the corresponding classification instructions are obtained. Finally, the multi-instruction real-time control of the robotic arm is realized under the classification instruction. The experimental verification results show that: The 10 subjects all realized the real-time multi-command control of the robotic arm, and the average recognition accuracy of each action reached more than 94%. The proposed system enriches the diversity of hybrid BCI and provides a theoretical basis and application foundation for the extended application of BCI in robotic arm control.

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