Classification of mental arithmetic and resting-state based on Ear-EEG

Soo In Choi; Ga Young Choi; Hyung Tak Lee; Han Jeong Hwang; Jaeyoung Shin

doi:10.1109/IWW-BCI.2018.8311525

Classification of mental arithmetic and resting-state based on Ear-EEG

Soo In Choi
, Ga Young Choi
, Hyung Tak Lee
, Han Jeong Hwang
, Jaeyoung Shin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

Electroencephalography (EEG) has been mainly utilized for developing brain-computer interface (BCI) systems. In recent, use of Ear-EEG measured around the ears has been proposed to enhance the practicality of conventional EEG-based BCI systems. Most of BCI systems based on Ear-EEG have used exogenous BCI paradigms employing external stimuli. In this study, we investigated the feasibility of using Ear-EEG in developing an endogenous BCI system that uses self-modulated brain signals. EEG data was measured while subjects performed mental arithmetic (MA) and baseline (BL) task. EEG data analysis was performed after dividing the whole brain area into four regions of interest (frontal, central, occipital, and ear area) to compare their EEG characteristics and classification performance. Similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs, and classification performance was insignificant between them, except occipital area (frontal: 72.6 %, central: 76.7 %, occipital: 82.6 % and ear: 75.6 %). From the results, we could confirm the possibility of using Ear-EEG for developing an endogenous BCI system.

Original language	English
Title of host publication	2018 6th International Conference on Brain-Computer Interface, BCI 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538625743
DOIs	https://doi.org/10.1109/IWW-BCI.2018.8311525
Publication status	Published - 2018 Mar 9
Externally published	Yes
Event	6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of Duration: 2018 Jan 15 → 2018 Jan 17

Publication series

Name	2018 6th International Conference on Brain-Computer Interface, BCI 2018
Volume	2018-January

Other

Other	6th International Conference on Brain-Computer Interface, BCI 2018
Country/Territory	Korea, Republic of
City	GangWon
Period	18/1/15 → 18/1/17

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

brain-computerinter interface (BCI)
Ear-EEG
electroencephalogram (EEG)
endogenous BCI system
mental arithmetic

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Behavioral Neuroscience

Access to Document

10.1109/IWW-BCI.2018.8311525

Cite this

Choi, S. I., Choi, G. Y., Lee, H. T., Hwang, H. J., & Shin, J. (2018). Classification of mental arithmetic and resting-state based on Ear-EEG. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (pp. 1-4). (2018 6th International Conference on Brain-Computer Interface, BCI 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311525

Classification of mental arithmetic and resting-state based on Ear-EEG. / Choi, Soo In; Choi, Ga Young; Lee, Hyung Tak et al.
2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (2018 6th International Conference on Brain-Computer Interface, BCI 2018; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choi, SI, Choi, GY, Lee, HT, Hwang, HJ & Shin, J 2018, Classification of mental arithmetic and resting-state based on Ear-EEG. in 2018 6th International Conference on Brain-Computer Interface, BCI 2018. 2018 6th International Conference on Brain-Computer Interface, BCI 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 6th International Conference on Brain-Computer Interface, BCI 2018, GangWon, Korea, Republic of, 18/1/15. https://doi.org/10.1109/IWW-BCI.2018.8311525

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abstract = "Electroencephalography (EEG) has been mainly utilized for developing brain-computer interface (BCI) systems. In recent, use of Ear-EEG measured around the ears has been proposed to enhance the practicality of conventional EEG-based BCI systems. Most of BCI systems based on Ear-EEG have used exogenous BCI paradigms employing external stimuli. In this study, we investigated the feasibility of using Ear-EEG in developing an endogenous BCI system that uses self-modulated brain signals. EEG data was measured while subjects performed mental arithmetic (MA) and baseline (BL) task. EEG data analysis was performed after dividing the whole brain area into four regions of interest (frontal, central, occipital, and ear area) to compare their EEG characteristics and classification performance. Similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs, and classification performance was insignificant between them, except occipital area (frontal: 72.6 \%, central: 76.7 \%, occipital: 82.6 \% and ear: 75.6 \%). From the results, we could confirm the possibility of using Ear-EEG for developing an endogenous BCI system.",

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Classification of mental arithmetic and resting-state based on Ear-EEG

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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