文章基本信息

标题：Using EEG Power Spectral Density To Run Mobile Robots
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作者：Neda pourghanbarkaleibar ; Peyman jabraelzade ; Alireza andalib 等
期刊名称：International Journal of Mechatronics, Electrical and Computer Technology
印刷版ISSN：2305-0543
出版年度：2018
卷号：8
期号：27
页码：3780-3788
出版社：Austrian E-Journals of Universal Scientific Organization
摘要：Nowadays, a considerable portion of research literature from universities worldwide focuses on the use of robots to enhance the abilities of physically challenged people. There are a variety of challenges in designing a brain-computer interface (BCI), the most important of which is the processing of the signals derived from human brain. Signal processing includes 3 Steps: feature extraction, feature reduction and classification. This paper focuses on processing EEG signals first and then transferring the results to mobile robots in order to develop intelligent wheelchair. Brain signals are derived from Physionet database. This paper employed power spectral density (PSD) and classification based on support vector machine (SVM) in order to extract the feature from the frequency domain database while ignoring the time domain. Controlling system in this research is an open-loop controller because the processed output instructions are displayed in two screens: (1) illustration of right movement and (0) illustration of left movement, with no feedback from the robot or the algorithm after the implementation. The output algorithm is used to direct the mobile robot: the mobile robot tilts to the right in case of a right hand movement while it tilts to the left in case of a left hand movement.
关键词：component; Brain Computer Interface System; EEG Signal; Extraction Feature; SVM Classification Introduction.