Selection of a subset of EEG channels using PCA to classify alcoholics and non-alcoholics

Kok Meng Ong; Kim Han Thung; Chong Yaw Wee; Raveendran Paramesran

Selection of a subset of EEG channels using PCA to classify alcoholics and non-alcoholics

Kok Meng Ong, Kim Han Thung, Chong Yaw Wee, Raveendran Paramesran

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

Abstract

The Principal Component Analysis (PCA) is proposed as feature selection method in choosing a subset of channels for Visual Evoked Potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics.

Original language	English
Title of host publication	Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages	4195-4198
Number of pages	4
Publication status	Published - 2005
Externally published	Yes
Event	International Conference of the IEEE Engineering in Medicine and Biology Society 2005 - Shanghai, China Duration: 1 Sept 2005 → 4 Sept 2005 Conference number: 27th https://ieeexplore.ieee.org/xpl/conhome/10755/proceeding (Proceedings)

Conference

Conference	International Conference of the IEEE Engineering in Medicine and Biology Society 2005
Abbreviated title	EMBC 2005
Country/Territory	China
City	Shanghai
Period	1/09/05 → 4/09/05
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10755/proceeding (Proceedings)

Cite this

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title = "Selection of a subset of EEG channels using PCA to classify alcoholics and non-alcoholics",

abstract = "The Principal Component Analysis (PCA) is proposed as feature selection method in choosing a subset of channels for Visual Evoked Potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics.",

author = "Ong, {Kok Meng} and Thung, {Kim Han} and Wee, {Chong Yaw} and Raveendran Paramesran",

year = "2005",

language = "English",

isbn = "0780387406",

pages = "4195--4198",

booktitle = "Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005",

note = "International Conference of the IEEE Engineering in Medicine and Biology Society 2005, EMBC 2005 ; Conference date: 01-09-2005 Through 04-09-2005",

url = "https://ieeexplore.ieee.org/xpl/conhome/10755/proceeding",

}

Ong, KM, Thung, KH, Wee, CY & Paramesran, R 2005, Selection of a subset of EEG channels using PCA to classify alcoholics and non-alcoholics. in Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. pp. 4195-4198, International Conference of the IEEE Engineering in Medicine and Biology Society 2005, Shanghai, China, 1/09/05.

Selection of a subset of EEG channels using PCA to classify alcoholics and non-alcoholics. / Ong, Kok Meng; Thung, Kim Han; Wee, Chong Yaw et al.
Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005. p. 4195-4198.

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

TY - GEN

T1 - Selection of a subset of EEG channels using PCA to classify alcoholics and non-alcoholics

AU - Ong, Kok Meng

AU - Thung, Kim Han

AU - Wee, Chong Yaw

AU - Paramesran, Raveendran

N1 - Conference code: 27th

PY - 2005

Y1 - 2005

N2 - The Principal Component Analysis (PCA) is proposed as feature selection method in choosing a subset of channels for Visual Evoked Potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics.

AB - The Principal Component Analysis (PCA) is proposed as feature selection method in choosing a subset of channels for Visual Evoked Potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics.

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M3 - Conference Paper

AN - SCOPUS:33846932574

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SN - 9780780387409

SP - 4195

EP - 4198

BT - Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005

T2 - International Conference of the IEEE Engineering in Medicine and Biology Society 2005

Y2 - 1 September 2005 through 4 September 2005

ER -

Selection of a subset of EEG channels using PCA to classify alcoholics and non-alcoholics

Abstract

Conference

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