Imirok: Real-time imitative robotic arm control for home robot applications

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, “teeth clenching” state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of “teeth clenching” condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93±0.03. Four subjects achieved the optimal criteria of writing the word “HI” which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control.

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Robotic Arm Control System Based on Brain-Muscle Mixed Signals

10.21203/rs.3.rs-528958/v1 ◽

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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Telemanipulation of an Articulated Robotic Arm using a Commercial Virtual Reality Controller

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3033 ◽

2020 ◽

Vol 6 (3) ◽

pp. 127-130

Author(s):

Max B. Schäfer ◽

Kent W. Stewart ◽

Nico Lösch ◽

Peter P. Pott

Keyword(s):

Virtual Reality ◽

Real Time ◽

Robotic Arm ◽

Input Device ◽

Robot Assisted ◽

Promising Alternative ◽

Input Devices ◽

Current Input ◽

Current Systems ◽

Robot Assisted Surgery

AbstractAccess to systems for robot-assisted surgery is limited due to high costs. To enable widespread use, numerous issues have to be addressed to improve and/or simplify their components. Current systems commonly use universal linkage-based input devices, and only a few applicationoriented and specialized designs are used. A versatile virtual reality controller is proposed as an alternative input device for the control of a seven degree of freedom articulated robotic arm. The real-time capabilities of the setup, replicating a system for robot-assisted teleoperated surgery, are investigated to assess suitability. Image-based assessment showed a considerable system latency of 81.7 ± 27.7 ms. However, due to its versatility, the virtual reality controller is a promising alternative to current input devices for research around medical telemanipulation systems.

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