Robotic arm control system based on brain-muscle mixed signals. (August 2022)

• Record Type: Journal Article
• Authors: Cheng, Liwei; Li, Duanling; Yu, Gongjing; Zhang, Zhonghai; Yu, Shuyue
• Is Part Of: Biomedical signal processing and control. Volume 77(2022)
• Publication Date: 2022-08
• Subjects: Brain computer interface -- Feature extraction -- Classification and identification -- Robotic arm; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103754
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Combining the characteristics of EEG and EMG, a hybrid BCI system is proposed. Set the action is simple and efficient, reducing the difficulty of identification. Proposed an EMG feature extraction algorithm based on feature average and FBP value. The method has a good decoding effect on the premise of ensuring the action. The system has good control precision and task completion rate in the application. 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 accuracy of the control instructions was more than 94%, the control results were good, and the success rate of the tasks was more than 80%. 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.