Hybrid EEG-EOG-based BCI system for Vehicle Control

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

doi:10.1109/BCI51272.2021.9385300

Hybrid EEG-EOG-based BCI system for Vehicle Control

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

Technical University of Denmark

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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 language	English
Title of host publication	Proceedings of 9^th International Winter Conference on Brain-Computer Interface
Number of pages	6
Publisher	IEEE
Publication date	2021
ISBN (Print)	978-1-7281-8486-9
DOIs	https://doi.org/10.1109/BCI51272.2021.9385300
Publication status	Published - 2021
Event	9^th International Winter Conference on Brain-Computer Interface - High1 Resort, Gangwon, Korea, Republic of Duration: 22 Feb 2021 → 24 Feb 2021

Conference

Conference	9^th International Winter Conference on Brain-Computer Interface
Location	High1 Resort
Country/Territory	Korea, Republic of
City	Gangwon
Period	22/02/2021 → 24/02/2021

Keywords

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

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10.1109/BCI51272.2021.9385300

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title = "Hybrid EEG-EOG-based BCI system for Vehicle Control",

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.",

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AU - Olesen, Simon Dahl Thorsager

AU - Das, Rig

AU - Olsson, Mathias Dizon

AU - Khan, Muhammad Ahmed

AU - Puthusserypady, Sadasivan

PY - 2021

Y1 - 2021

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AB - 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.

KW - Brain computer interfaces (BCI)

KW - Electrooculography (EOG)

KW - Electroencephalogram (EEG)

KW - Motor Imagery (MI)

KW - Wheelchair control

U2 - 10.1109/BCI51272.2021.9385300

DO - 10.1109/BCI51272.2021.9385300

M3 - Article in proceedings

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ER -

Hybrid EEG-EOG-based BCI system for Vehicle Control

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Conference

Keywords

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