Classification of motor imagery for Ear-EEG based brain-computer interface

Yong Jeong Kim, No Sang Kwak, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Brain-computer interface (BCI) researchers have shown an increased interest in the development of ear-electroencephalography (EEG), which is a method for measuring EEG signals in the ear or around the outer ear, to provide a more convenient BCI system to users. However, the ear-EEG studies have researched mostly targeting on a visual/auditory stimuli-based BCI system or a drowsiness detection system. To the best of our knowledge, there is no study on a motor-imagery (MI) detection system based on ear-EEG. MI is one of the mostly used paradigms in BCI because it does not need any external stimuli. MI that associated with ear-EEG could facilitate useful BCI applications in real-world. Hence, in this study, we aim to investigate a feasibility of the MI classification using ear-around EEG signals. We proposed a common spatial pattern (CSP)-based frequency-band optimization algorithm and compared it with three existing methods. The best classification results for two datasets are 71.8% and 68.07%, respectively, using the ear-around EEG signals (cf. 92.40% and 91.64% using motor-area EEG signals).

Original languageEnglish
Title of host publication2018 6th International Conference on Brain-Computer Interface, BCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-2
Number of pages2
Volume2018-January
ISBN (Electronic)9781538625743
DOIs
Publication statusPublished - 2018 Mar 9
Event6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of
Duration: 2018 Jan 152018 Jan 17

Other

Other6th International Conference on Brain-Computer Interface, BCI 2018
CountryKorea, Republic of
CityGangWon
Period18/1/1518/1/17

Fingerprint

Brain-Computer Interfaces
Brain computer interface
Imagery (Psychotherapy)
Electroencephalography
Ear
Computer Systems
Frequency bands
External Ear
Sleep Stages
Motor Cortex

Keywords

  • brain-computer interface
  • ear-EEG
  • motor imagery

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Behavioral Neuroscience

Cite this

Kim, Y. J., Kwak, N. S., & Lee, S. W. (2018). Classification of motor imagery for Ear-EEG based brain-computer interface. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (Vol. 2018-January, pp. 1-2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311517

Classification of motor imagery for Ear-EEG based brain-computer interface. / Kim, Yong Jeong; Kwak, No Sang; Lee, Seong Whan.

2018 6th International Conference on Brain-Computer Interface, BCI 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-2.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, YJ, Kwak, NS & Lee, SW 2018, Classification of motor imagery for Ear-EEG based brain-computer interface. in 2018 6th International Conference on Brain-Computer Interface, BCI 2018. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-2, 6th International Conference on Brain-Computer Interface, BCI 2018, GangWon, Korea, Republic of, 18/1/15. https://doi.org/10.1109/IWW-BCI.2018.8311517
Kim YJ, Kwak NS, Lee SW. Classification of motor imagery for Ear-EEG based brain-computer interface. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-2 https://doi.org/10.1109/IWW-BCI.2018.8311517
Kim, Yong Jeong ; Kwak, No Sang ; Lee, Seong Whan. / Classification of motor imagery for Ear-EEG based brain-computer interface. 2018 6th International Conference on Brain-Computer Interface, BCI 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-2
@inproceedings{3f382a9435904d61ace2588aa2c74eac,
title = "Classification of motor imagery for Ear-EEG based brain-computer interface",
abstract = "Brain-computer interface (BCI) researchers have shown an increased interest in the development of ear-electroencephalography (EEG), which is a method for measuring EEG signals in the ear or around the outer ear, to provide a more convenient BCI system to users. However, the ear-EEG studies have researched mostly targeting on a visual/auditory stimuli-based BCI system or a drowsiness detection system. To the best of our knowledge, there is no study on a motor-imagery (MI) detection system based on ear-EEG. MI is one of the mostly used paradigms in BCI because it does not need any external stimuli. MI that associated with ear-EEG could facilitate useful BCI applications in real-world. Hence, in this study, we aim to investigate a feasibility of the MI classification using ear-around EEG signals. We proposed a common spatial pattern (CSP)-based frequency-band optimization algorithm and compared it with three existing methods. The best classification results for two datasets are 71.8{\%} and 68.07{\%}, respectively, using the ear-around EEG signals (cf. 92.40{\%} and 91.64{\%} using motor-area EEG signals).",
keywords = "brain-computer interface, ear-EEG, motor imagery",
author = "Kim, {Yong Jeong} and Kwak, {No Sang} and Lee, {Seong Whan}",
year = "2018",
month = "3",
day = "9",
doi = "10.1109/IWW-BCI.2018.8311517",
language = "English",
volume = "2018-January",
pages = "1--2",
booktitle = "2018 6th International Conference on Brain-Computer Interface, BCI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Classification of motor imagery for Ear-EEG based brain-computer interface

AU - Kim, Yong Jeong

AU - Kwak, No Sang

AU - Lee, Seong Whan

PY - 2018/3/9

Y1 - 2018/3/9

N2 - Brain-computer interface (BCI) researchers have shown an increased interest in the development of ear-electroencephalography (EEG), which is a method for measuring EEG signals in the ear or around the outer ear, to provide a more convenient BCI system to users. However, the ear-EEG studies have researched mostly targeting on a visual/auditory stimuli-based BCI system or a drowsiness detection system. To the best of our knowledge, there is no study on a motor-imagery (MI) detection system based on ear-EEG. MI is one of the mostly used paradigms in BCI because it does not need any external stimuli. MI that associated with ear-EEG could facilitate useful BCI applications in real-world. Hence, in this study, we aim to investigate a feasibility of the MI classification using ear-around EEG signals. We proposed a common spatial pattern (CSP)-based frequency-band optimization algorithm and compared it with three existing methods. The best classification results for two datasets are 71.8% and 68.07%, respectively, using the ear-around EEG signals (cf. 92.40% and 91.64% using motor-area EEG signals).

AB - Brain-computer interface (BCI) researchers have shown an increased interest in the development of ear-electroencephalography (EEG), which is a method for measuring EEG signals in the ear or around the outer ear, to provide a more convenient BCI system to users. However, the ear-EEG studies have researched mostly targeting on a visual/auditory stimuli-based BCI system or a drowsiness detection system. To the best of our knowledge, there is no study on a motor-imagery (MI) detection system based on ear-EEG. MI is one of the mostly used paradigms in BCI because it does not need any external stimuli. MI that associated with ear-EEG could facilitate useful BCI applications in real-world. Hence, in this study, we aim to investigate a feasibility of the MI classification using ear-around EEG signals. We proposed a common spatial pattern (CSP)-based frequency-band optimization algorithm and compared it with three existing methods. The best classification results for two datasets are 71.8% and 68.07%, respectively, using the ear-around EEG signals (cf. 92.40% and 91.64% using motor-area EEG signals).

KW - brain-computer interface

KW - ear-EEG

KW - motor imagery

UR - http://www.scopus.com/inward/record.url?scp=85050819916&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050819916&partnerID=8YFLogxK

U2 - 10.1109/IWW-BCI.2018.8311517

DO - 10.1109/IWW-BCI.2018.8311517

M3 - Conference contribution

AN - SCOPUS:85050819916

VL - 2018-January

SP - 1

EP - 2

BT - 2018 6th International Conference on Brain-Computer Interface, BCI 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -