{"title":"基于脑电图的脑机接口:提取α波应用于移动机器人","authors":"M. Mahmud, D. Hawellek, Aleksander Valjamae","doi":"10.1109/AT-EQUAL.2009.17","DOIUrl":null,"url":null,"abstract":"The increasing number of signal processing tools for highly parallel neurophysiological recordings opens up new avenues for connecting technologies directly to neuronal processes. As the understanding is taking a better shape, lot more work to perform is coming up. A simple brain-machine interface may be able to reestablish the broken loop of the persons with motor dysfunction. With time the brain-machine interfacing is growing more complex due to the increased availability of instruments and processes for implementation. In this work, as a proof-of-principle we established a brain-machine interface through a few simple processes to control a robotic device using the alpha wave’s event-related synchronization and event-related de-synchronization extracted from EEG.","PeriodicalId":407640,"journal":{"name":"2009 Advanced Technologies for Enhanced Quality of Life","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"A Brain-Machine Interface Based on EEG: Extracted Alpha Waves Applied to Mobile Robot\",\"authors\":\"M. Mahmud, D. Hawellek, Aleksander Valjamae\",\"doi\":\"10.1109/AT-EQUAL.2009.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing number of signal processing tools for highly parallel neurophysiological recordings opens up new avenues for connecting technologies directly to neuronal processes. As the understanding is taking a better shape, lot more work to perform is coming up. A simple brain-machine interface may be able to reestablish the broken loop of the persons with motor dysfunction. With time the brain-machine interfacing is growing more complex due to the increased availability of instruments and processes for implementation. In this work, as a proof-of-principle we established a brain-machine interface through a few simple processes to control a robotic device using the alpha wave’s event-related synchronization and event-related de-synchronization extracted from EEG.\",\"PeriodicalId\":407640,\"journal\":{\"name\":\"2009 Advanced Technologies for Enhanced Quality of Life\",\"volume\":\"121 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Advanced Technologies for Enhanced Quality of Life\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AT-EQUAL.2009.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Advanced Technologies for Enhanced Quality of Life","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AT-EQUAL.2009.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Brain-Machine Interface Based on EEG: Extracted Alpha Waves Applied to Mobile Robot
The increasing number of signal processing tools for highly parallel neurophysiological recordings opens up new avenues for connecting technologies directly to neuronal processes. As the understanding is taking a better shape, lot more work to perform is coming up. A simple brain-machine interface may be able to reestablish the broken loop of the persons with motor dysfunction. With time the brain-machine interfacing is growing more complex due to the increased availability of instruments and processes for implementation. In this work, as a proof-of-principle we established a brain-machine interface through a few simple processes to control a robotic device using the alpha wave’s event-related synchronization and event-related de-synchronization extracted from EEG.