EEG based brain-machine interface for navigation of robotic device

  • Mufti Mahmud*
  • , David Hawellek
  • , Alessandra Bertoldo
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

Abstract

The highly parallel neurophysiological recordings and the increasing number of signal processing tools open up new avenues for connecting technologies directly to neuronal processes. As the understanding of the neuronal signals is taking a better shape, lot more work to perform is coming up to properly interpret and use these signals for brain-machine interfaces. 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, the author proposes a brain-machine interface model through a few simple processes for automated navigation and control of robotic device using the extracted features from the EEG signals based on saccadic eye movement tasks.

Original languageEnglish
Title of host publication2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2010
Pages168-172
Number of pages5
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2010 - Tokyo, Japan
Duration: 26 Sep 201029 Sep 2010

Publication series

Name2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2010

Conference

Conference2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2010
Country/TerritoryJapan
CityTokyo
Period26/09/1029/09/10

Keywords

  • Brain-machine interface
  • Electroencephalogram
  • Neurophysiological recording
  • Saccadic eye movement
  • Signal processing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering

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