Real-time gesture recognition with Grael EEG amplifier and deep learning

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Real-time gesture recognition with Grael EEG amplifier and deep learning
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                        Liu XiaoguangLiu Xiaoguang
1.College of Electronic and Information Engineering, Hebei University，Baoding 071002, China； 2.Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China
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Zhang MingjinZhang Mingjin
1.College of Electronic and Information Engineering, Hebei University，Baoding 071002, China； 2.Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China
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Wang JiaweiWang Jiawei
1.College of Electronic and Information Engineering, Hebei University，Baoding 071002, China； 2.Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China
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Liang TieLiang Tie
1.College of Electronic and Information Engineering, Hebei University，Baoding 071002, China； 2.Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China
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Li JunLi Jun
1.College of Electronic and Information Engineering, Hebei University，Baoding 071002, China； 2.Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China
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Liu XiulingLiu Xiuling
1.College of Electronic and Information Engineering, Hebei University，Baoding 071002, China； 2.Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China
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Affiliation:1.College of Electronic and Information Engineering, Hebei University，Baoding 071002, China; 2.Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China
Clc Number:TP391.41
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Abstract:

Gesture recognition is the key to human-computer interaction. In order to better realize the fusion of EEG and EMG signals and accurately recognize human motion, this paper established a research system of real-time gesture detection and recognition based on Grael EEG amplifier. The surface electromyography (sEMG) signals of eight different gestures from five channels were collected by Grael EEG amplifier and Curry8 system, and the collected sEMG signals were preprocessed by filtering and denoising, sliding window segmentation and feature extraction. Finally, several commonly used classifiers and convolutional neural network (CNN) are used to classify and recognize sEMG signals of different gestures in real time. The results show that the recognition accuracy of CNN is the highest, reaching 92.98%. After 30 times of real-time recognition and detection for each gesture, the results show that the recognition delay is about 1~1.5 s, and the accuracy of real-time recognition can be up to 90%. This system provides a feasible method to study the fusion of EEG and EMG signals in the future, and shows great potential and application space in human-computer interaction.

Key words:Grael EEG amplifier;surface EMG signal;gesture recognition;convolutional neural network

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Online: February 07,2024
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