{"title":"Design of a robotic wheelchair with a motor imagery based brain-computer interface","authors":"Keun-Tae Kim, T. Carlson, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2013.6506625","DOIUrl":null,"url":null,"abstract":"This paper presents a prototype for an electro-encephalogram (EEG) based brain-actuated wheelchair system using motor imagery. To overcome some of the limitations of other previous works, such as gaze dependence and unnecessary stops, five commands (left, left-diagonal, right, right-diagonal, and forward) were decoded based on the motor imagery correlates in EEG signals. Also, the system was modularized into three components: BCI control, and network. On the basis of the conclusions, we can expect a robust brain-actuated wheelchair system, which can allow the user's intention to control the wheelchair in multi-directional movements, thereby increasing the user's authority compared with many of the alternative approaches.","PeriodicalId":129758,"journal":{"name":"2013 International Winter Workshop on Brain-Computer Interface (BCI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Winter Workshop on Brain-Computer Interface (BCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWW-BCI.2013.6506625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24
Abstract
This paper presents a prototype for an electro-encephalogram (EEG) based brain-actuated wheelchair system using motor imagery. To overcome some of the limitations of other previous works, such as gaze dependence and unnecessary stops, five commands (left, left-diagonal, right, right-diagonal, and forward) were decoded based on the motor imagery correlates in EEG signals. Also, the system was modularized into three components: BCI control, and network. On the basis of the conclusions, we can expect a robust brain-actuated wheelchair system, which can allow the user's intention to control the wheelchair in multi-directional movements, thereby increasing the user's authority compared with many of the alternative approaches.