Time series classification for EEG eye state identification based on incremental attribute learning

Ting Wang; Sheng Uei Guan; Ka Lok Man; T. O. Ting

doi:10.1109/IS3C.2014.52

Time series classification for EEG eye state identification based on incremental attribute learning

Ting Wang, Sheng Uei Guan, Ka Lok Man, T. O. Ting

University of Liverpool

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

14 Citations (Scopus)

Abstract

Electroencephalography (EEG) eye state classification is important and useful to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper proposes a novel EEG eye state identification approach based on Incremental Attribute Learning (IAL). Experimental results show that, with proper feature extraction and feature ordering, IAL can not only cope with time series classification problems efficiently, but also exhibit better classification performance in terms of classification error rates in comparison with other approaches.

Original language	English
Title of host publication	Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014
Publisher	IEEE Computer Society
Pages	158-161
Number of pages	4
ISBN (Print)	9781479952779
DOIs	https://doi.org/10.1109/IS3C.2014.52
Publication status	Published - 2014
Event	2nd International Symposium on Computer, Consumer and Control, IS3C 2014 - Taichung, Taiwan, Province of China Duration: 10 Jun 2014 → 12 Jun 2014

Publication series

Name	Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014

Conference

Conference	2nd International Symposium on Computer, Consumer and Control, IS3C 2014
Country/Territory	Taiwan, Province of China
City	Taichung
Period	10/06/14 → 12/06/14

Keywords

Electroencephalography
Eye State Identification
Incremental Attribute Learning
Neural Networks
Time Series Classification

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10.1109/IS3C.2014.52

Cite this

Wang, T., Guan, S. U., Man, K. L., & Ting, T. O. (2014). Time series classification for EEG eye state identification based on incremental attribute learning. In Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014 (pp. 158-161). Article 6845484 (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014). IEEE Computer Society. https://doi.org/10.1109/IS3C.2014.52

Wang, Ting ; Guan, Sheng Uei ; Man, Ka Lok et al. / Time series classification for EEG eye state identification based on incremental attribute learning. Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014. IEEE Computer Society, 2014. pp. 158-161 (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014).

@inproceedings{2113e974572f4f7ab6f38d308216bb74,

title = "Time series classification for EEG eye state identification based on incremental attribute learning",

abstract = "Electroencephalography (EEG) eye state classification is important and useful to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper proposes a novel EEG eye state identification approach based on Incremental Attribute Learning (IAL). Experimental results show that, with proper feature extraction and feature ordering, IAL can not only cope with time series classification problems efficiently, but also exhibit better classification performance in terms of classification error rates in comparison with other approaches.",

keywords = "Electroencephalography, Eye State Identification, Incremental Attribute Learning, Neural Networks, Time Series Classification",

author = "Ting Wang and Guan, {Sheng Uei} and Man, {Ka Lok} and Ting, {T. O.}",

year = "2014",

doi = "10.1109/IS3C.2014.52",

language = "English",

isbn = "9781479952779",

series = "Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014",

publisher = "IEEE Computer Society",

pages = "158--161",

booktitle = "Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014",

note = "2nd International Symposium on Computer, Consumer and Control, IS3C 2014 ; Conference date: 10-06-2014 Through 12-06-2014",

}

Wang, T, Guan, SU , Man, KL & Ting, TO 2014, Time series classification for EEG eye state identification based on incremental attribute learning. in Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014., 6845484, Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014, IEEE Computer Society, pp. 158-161, 2nd International Symposium on Computer, Consumer and Control, IS3C 2014, Taichung, Taiwan, Province of China, 10/06/14. https://doi.org/10.1109/IS3C.2014.52

Time series classification for EEG eye state identification based on incremental attribute learning. / Wang, Ting; Guan, Sheng Uei ; Man, Ka Lok et al.
Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014. IEEE Computer Society, 2014. p. 158-161 6845484 (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Time series classification for EEG eye state identification based on incremental attribute learning

AU - Wang, Ting

AU - Guan, Sheng Uei

AU - Man, Ka Lok

AU - Ting, T. O.

PY - 2014

Y1 - 2014

N2 - Electroencephalography (EEG) eye state classification is important and useful to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper proposes a novel EEG eye state identification approach based on Incremental Attribute Learning (IAL). Experimental results show that, with proper feature extraction and feature ordering, IAL can not only cope with time series classification problems efficiently, but also exhibit better classification performance in terms of classification error rates in comparison with other approaches.

AB - Electroencephalography (EEG) eye state classification is important and useful to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper proposes a novel EEG eye state identification approach based on Incremental Attribute Learning (IAL). Experimental results show that, with proper feature extraction and feature ordering, IAL can not only cope with time series classification problems efficiently, but also exhibit better classification performance in terms of classification error rates in comparison with other approaches.

KW - Electroencephalography

KW - Eye State Identification

KW - Incremental Attribute Learning

KW - Neural Networks

KW - Time Series Classification

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U2 - 10.1109/IS3C.2014.52

DO - 10.1109/IS3C.2014.52

M3 - Conference Proceeding

AN - SCOPUS:84904431372

SN - 9781479952779

T3 - Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014

SP - 158

EP - 161

BT - Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014

PB - IEEE Computer Society

T2 - 2nd International Symposium on Computer, Consumer and Control, IS3C 2014

Y2 - 10 June 2014 through 12 June 2014

ER -

Wang T, Guan SU , Man KL, Ting TO. Time series classification for EEG eye state identification based on incremental attribute learning. In Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014. IEEE Computer Society. 2014. p. 158-161. 6845484. (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014). doi: 10.1109/IS3C.2014.52

Time series classification for EEG eye state identification based on incremental attribute learning

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