Novel BCI classification method using cross-channel-region CSP features

Yongkoo Park; Wonzoo Chung

doi:10.1109/IWW-BCI.2018.8311528

Novel BCI classification method using cross-channel-region CSP features

Yongkoo Park, Wonzoo Chung

Department of Computer Science and Engineering

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

12 Citations (Scopus)

Abstract

In this paper, we explore locally generated cross-channel-region CSP features to improve motor imagery classification in EEG-based BCIs. We set several clustered sub-channel regions covering the entire measured channels and extract CSP features by cross-combining the sub-channel regions with each single channel. The features generated by this cross-channel-region combinations have regional information on sensor space for motor imagery and can be used to improve classification accuracy when fed to LS-SVM classifier. The performance improvement of the proposed algorithm is verified by simulations.

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.2018.8311528
Publication status	Published - 2018 Mar 9
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

Keywords

Brain-Computer Interfaces (BCIs)
Common Spatial Pattern (CSP)
cross-channel-region combinations
electroencephalography (EEG)

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Behavioral Neuroscience

Access to Document

10.1109/IWW-BCI.2018.8311528

Cite this

Park, Y., & Chung, W. (2018). Novel BCI classification method using cross-channel-region CSP features. 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.2018.8311528

Novel BCI classification method using cross-channel-region CSP features. / Park, Yongkoo; Chung, Wonzoo.
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

Park, Y & Chung, W 2018, Novel BCI classification method using cross-channel-region CSP features. 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.2018.8311528

@inproceedings{2bfbbcd67d7446149676335fa2b5ad84,

title = "Novel BCI classification method using cross-channel-region CSP features",

abstract = "In this paper, we explore locally generated cross-channel-region CSP features to improve motor imagery classification in EEG-based BCIs. We set several clustered sub-channel regions covering the entire measured channels and extract CSP features by cross-combining the sub-channel regions with each single channel. The features generated by this cross-channel-region combinations have regional information on sensor space for motor imagery and can be used to improve classification accuracy when fed to LS-SVM classifier. The performance improvement of the proposed algorithm is verified by simulations.",

keywords = "Brain-Computer Interfaces (BCIs), Common Spatial Pattern (CSP), cross-channel-region combinations, electroencephalography (EEG)",

author = "Yongkoo Park and Wonzoo Chung",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).; 6th International Conference on Brain-Computer Interface, BCI 2018 ; Conference date: 15-01-2018 Through 17-01-2018",

year = "2018",

month = mar,

day = "9",

doi = "10.1109/IWW-BCI.2018.8311528",

language = "English",

series = "2018 6th International Conference on Brain-Computer Interface, BCI 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--4",

booktitle = "2018 6th International Conference on Brain-Computer Interface, BCI 2018",

}

TY - GEN

T1 - Novel BCI classification method using cross-channel-region CSP features

AU - Park, Yongkoo

AU - Chung, Wonzoo

N1 - Funding Information: ACKNOWLEDGMENT This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).

PY - 2018/3/9

Y1 - 2018/3/9

N2 - In this paper, we explore locally generated cross-channel-region CSP features to improve motor imagery classification in EEG-based BCIs. We set several clustered sub-channel regions covering the entire measured channels and extract CSP features by cross-combining the sub-channel regions with each single channel. The features generated by this cross-channel-region combinations have regional information on sensor space for motor imagery and can be used to improve classification accuracy when fed to LS-SVM classifier. The performance improvement of the proposed algorithm is verified by simulations.

AB - In this paper, we explore locally generated cross-channel-region CSP features to improve motor imagery classification in EEG-based BCIs. We set several clustered sub-channel regions covering the entire measured channels and extract CSP features by cross-combining the sub-channel regions with each single channel. The features generated by this cross-channel-region combinations have regional information on sensor space for motor imagery and can be used to improve classification accuracy when fed to LS-SVM classifier. The performance improvement of the proposed algorithm is verified by simulations.

KW - Brain-Computer Interfaces (BCIs)

KW - Common Spatial Pattern (CSP)

KW - cross-channel-region combinations

KW - electroencephalography (EEG)

UR - http://www.scopus.com/inward/record.url?scp=85050806107&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050806107&partnerID=8YFLogxK

U2 - 10.1109/IWW-BCI.2018.8311528

DO - 10.1109/IWW-BCI.2018.8311528

M3 - Conference contribution

AN - SCOPUS:85050806107

T3 - 2018 6th International Conference on Brain-Computer Interface, BCI 2018

SP - 1

EP - 4

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Brain-Computer Interface, BCI 2018

Y2 - 15 January 2018 through 17 January 2018

ER -

Novel BCI classification method using cross-channel-region CSP features

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this