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Robot control based on EMG correlates of facial expressions using a BCI system

文献类型： 会议论文

第一作者： ádám Gyula Nemes

作者： ádám Gyula Nemes 1 ; Gy?rgy Eigner 2 ; Béla Weiss 3 ;

作者机构： 1.Physiological Controls Research Center, óbuda University, Budapest, Bécsi street 96/B|Applied Informatics and Applied Mathematics Doctoral School, óbuda University, óbuda University, Budapest, Bécsi street 96/B|Brain Research Centre, Research Centre for Natural Sciences, Magyar tudósok k?rútja 2, Budapest, Hungary

2.Physiological Controls Research Center, óbuda University, Budapest, Bécsi street 96/B|Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics óbuda University, Budapest, Bécsi street 96/B

3.Applied Informatics and Applied Mathematics Doctoral School, óbuda University, óbuda University, Budapest, Bécsi street 96/B

关键词： Convolution;Navigation;Error analysis;Robot control;Support vector machine classification;Feature extraction;Brain-computer interfaces

会议名称： IEEE International Conference on Systems, Man, and Cybernetics

主办单位：

页码： 1506-1511

摘要： Electromyography-based control applications became popular in recent days since they can be implemented using cheap brain-computer interface devices. In the past, for classifying electromyogram data, human experts would extract features from raw signals manually. Unfortunately, this is a time-consuming process and requires extensive domain knowledge. With the advancement of artificial intelligence, such procedures can be done automatically using deep learning architectures. In this study, we designed a deep convolutional network to classify electromyogram signals. Furthermore, a support vector machine-based classifier was also implemented as a reference system. The goal of this study was to develop a control architecture, in which the user interface is equipped with a cheap commercial brain-computer interface system for mobile robot control. We collected a considerable amount of electromyogram signals during different facial expressions that can be used as intervention signals. Finally, we implemented an online real-time control method based on the deep convolutional network. The system enables the user to drive a mobile robot platform solely with electromyogram signals. Manipulating the designed system, after a short initial practice period, the users could navigate through a predefined path with a low error rate.

分类号： tp13-53

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