On association study of scalp EEG data channels under different circumstances

Jingyi Zheng; Mingli Liang; Arne Ekstrom; Linqiang Ge; Wei Yu; Fushing Hsieh

doi:10.1007/978-3-319-94268-1_56

On association study of scalp EEG data channels under different circumstances

Jingyi Zheng, Mingli Liang, Arne Ekstrom, Linqiang Ge, Wei Yu, Fushing Hsieh

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

8 Scopus citations

Abstract

Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain using different electrodes, which are considered as the EEG channels that are placed on scalp. In this paper, we propose an effective information processing approach to explore the association among EEG channels under different circumstances. Particularly, we design four different experimental scenarios and record the EEG signals under motions of eye-opening and body-movement. With sequences of data collected in time order, we first compute the mutual conditional entropy to measure the association between two electrodes. Using the hierarchical clustering tree and data mechanics algorithm, we could effectively identify the association between particular EEG channels under certain motion scenarios. We also implement the weighted random forest to further classify the classes (experimental scenarios) of the EEG time series. Our evaluation results show that we could successfully classify the particular motions with given EEG data series.

Original language	English (US)
Title of host publication	Wireless Algorithms, Systems, and Applications - 13th International Conference, WASA 2018, Proceedings
Editors	Wei Cheng, Wei Li, Sriram Chellappan
Publisher	Springer-Verlag
Pages	683-695
Number of pages	13
ISBN (Print)	9783319942674
DOIs	https://doi.org/10.1007/978-3-319-94268-1_56
State	Published - 2018
Externally published	Yes
Event	13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018 - Tianjin, China Duration: Jun 20 2018 → Jun 22 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10874 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018
Country/Territory	China
City	Tianjin
Period	6/20/18 → 6/22/18

Keywords

Algorithm design
EEG channels
Information processing
Mutual conditional entropy
Weighted random forest

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-94268-1_56

Cite this

Zheng, J., Liang, M., Ekstrom, A., Ge, L., Yu, W., & Hsieh, F. (2018). On association study of scalp EEG data channels under different circumstances. In W. Cheng, W. Li, & S. Chellappan (Eds.), Wireless Algorithms, Systems, and Applications - 13th International Conference, WASA 2018, Proceedings (pp. 683-695). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10874 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-94268-1_56

On association study of scalp EEG data channels under different circumstances. / Zheng, Jingyi; Liang, Mingli; Ekstrom, Arne et al.
Wireless Algorithms, Systems, and Applications - 13th International Conference, WASA 2018, Proceedings. ed. / Wei Cheng; Wei Li; Sriram Chellappan. Springer-Verlag, 2018. p. 683-695 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10874 LNCS).

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

Zheng, J, Liang, M, Ekstrom, A, Ge, L, Yu, W & Hsieh, F 2018, On association study of scalp EEG data channels under different circumstances. in W Cheng, W Li & S Chellappan (eds), Wireless Algorithms, Systems, and Applications - 13th International Conference, WASA 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10874 LNCS, Springer-Verlag, pp. 683-695, 13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018, Tianjin, China, 6/20/18. https://doi.org/10.1007/978-3-319-94268-1_56

Zheng J, Liang M, Ekstrom A, Ge L, Yu W, Hsieh F. On association study of scalp EEG data channels under different circumstances. In Cheng W, Li W, Chellappan S, editors, Wireless Algorithms, Systems, and Applications - 13th International Conference, WASA 2018, Proceedings. Springer-Verlag. 2018. p. 683-695. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-94268-1_56

Zheng, Jingyi ; Liang, Mingli ; Ekstrom, Arne et al. / On association study of scalp EEG data channels under different circumstances. Wireless Algorithms, Systems, and Applications - 13th International Conference, WASA 2018, Proceedings. editor / Wei Cheng ; Wei Li ; Sriram Chellappan. Springer-Verlag, 2018. pp. 683-695 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AB - Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain using different electrodes, which are considered as the EEG channels that are placed on scalp. In this paper, we propose an effective information processing approach to explore the association among EEG channels under different circumstances. Particularly, we design four different experimental scenarios and record the EEG signals under motions of eye-opening and body-movement. With sequences of data collected in time order, we first compute the mutual conditional entropy to measure the association between two electrodes. Using the hierarchical clustering tree and data mechanics algorithm, we could effectively identify the association between particular EEG channels under certain motion scenarios. We also implement the weighted random forest to further classify the classes (experimental scenarios) of the EEG time series. Our evaluation results show that we could successfully classify the particular motions with given EEG data series.

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On association study of scalp EEG data channels under different circumstances

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Keywords

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