Hybrid EEG-EOG-based BCI system for Vehicle Control

Simon Dahl Thorsager Olesen, Rig Das, Mathias Dizon Olsson, Muhammad Ahmed Khan, Sadasivan Puthusserypady

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Brain-Computer Interfaces (BCI) has become a medium of communication and interaction for disabled people. Electroencephalography (EEG) signals are one of the most widely used for such BCI systems. Over the past decade or so, electrooculography (EOG) signals have shown tremendous potential to complement EEG based BCI systems. In this paper, we investigate the possibility of a hybrid BCI system, combining the EEG and EOG signals, for remotely controlling a vehicle, such as a wheelchair, using machine learning technique. Motor Imagery (MI) EEG signals and EOG signals are combined to design this robust and computationally faster system. The proposed system is trained and tested on 13 in-house subject's data and it is able to achieve an average accuracy of 87.3%, where, 3 of the subjects produced more than 90% accuracy.
Original languageEnglish
Title of host publicationProceedings of 9th International Winter Conference on Brain-Computer Interface
Number of pages6
PublisherIEEE
Publication date2021
ISBN (Print)978-1-7281-8486-9
DOIs
Publication statusPublished - 2021
Event9th International Winter Conference on Brain-Computer Interface - High1 Resort, Gangwon, Korea, Republic of
Duration: 22 Feb 202124 Feb 2021

Conference

Conference9th International Winter Conference on Brain-Computer Interface
LocationHigh1 Resort
CountryKorea, Republic of
CityGangwon
Period22/02/202124/02/2021
Series2021 9th International Winter Conference on Brain-computer Interface (bci)
ISSN2572-7672

Keywords

  • Brain computer interfaces (BCI)
  • Electrooculography (EOG)
  • Electroencephalogram (EEG)
  • Motor Imagery (MI)
  • Wheelchair control

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