{"title":"Speed control of Festo Robotino mobile robot using NeuroSky MindWave EEG headset based brain-computer interface","authors":"J. Katona, T. Ujbanyi, Gergely Sziladi, A. Kovari","doi":"10.1109/COGINFOCOM.2016.7804557","DOIUrl":null,"url":null,"abstract":"This article presents the development, implementation and testing of a brain-computer interface (BCI) system, which enables the speed control of the mobile robot called Robotino, manufactured by Festo Didactic. The BCI system was implemented, and the results of the BCI system were evaluated during a students' project, based on the project-based learning methodology. Speed control has been achieved by utilization of NeuroSky MindWave EEG headset-based electroencephalogram (EEG) method, by processing brain bioelectric signals measured on the frontal lobe. Tests of the system evolved by using the brain-computer interface have been performed and evaluated, both regarding implementing speed control and user's experiences, which have been finished with positive results.","PeriodicalId":440408,"journal":{"name":"2016 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"69","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COGINFOCOM.2016.7804557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 69
Abstract
This article presents the development, implementation and testing of a brain-computer interface (BCI) system, which enables the speed control of the mobile robot called Robotino, manufactured by Festo Didactic. The BCI system was implemented, and the results of the BCI system were evaluated during a students' project, based on the project-based learning methodology. Speed control has been achieved by utilization of NeuroSky MindWave EEG headset-based electroencephalogram (EEG) method, by processing brain bioelectric signals measured on the frontal lobe. Tests of the system evolved by using the brain-computer interface have been performed and evaluated, both regarding implementing speed control and user's experiences, which have been finished with positive results.