A design of the EEG feature detection and condition classification

Junko Murakami; Shin Ichi Ito; Yasue Mitsukura; Jianting Cao; Minoru Fukumi

doi:10.1109/SICE.2007.4421464

A design of the EEG feature detection and condition classification

Junko Murakami, Shin Ichi Ito, Yasue Mitsukura, Jianting Cao, Minoru Fukumi

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, we classify the human conditions (before and after meal, before and after smoking) and extract the frequency feature of conditions by using the electroencephalograms (EEG). First, we measure the EEG data. Then, we classify the conditions by using the principal component analysis (PCA). Moreover, the EEG data is reconstructed by using the questionnaires and the result of classification. From the result, we consider ideal circumstance for the EEG measurement. Finally, the EEG data is decompressed to consider the EEG features of conditions. Then, in order to show the effectiveness of the proposed method, computer simulations are done.

本文言語	English
ホスト出版物のタイトル	SICE Annual Conference, SICE 2007
出版社	Society of Instrument and Control Engineers (SICE)
ページ	2798-2803
ページ数	6
ISBN（印刷版）	4907764286, 9784907764289
DOI	https://doi.org/10.1109/SICE.2007.4421464
出版ステータス	Published - 2007
外部発表	はい
イベント	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan 継続期間: 2007 9月 17 → 2007 9月 20

出版物シリーズ

名前	Proceedings of the SICE Annual Conference

Other

Other	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
国/地域	Japan
City	Takamatsu
Period	07/9/17 → 07/9/20

ASJC Scopus subject areas

制御およびシステム工学
コンピュータサイエンスの応用
電子工学および電気工学

文献へのアクセス

10.1109/SICE.2007.4421464

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引用スタイル

Murakami, J, Ito, SI, Mitsukura, Y, Cao, J & Fukumi, M 2007, A design of the EEG feature detection and condition classification. in SICE Annual Conference, SICE 2007., 4421464, Proceedings of the SICE Annual Conference, Society of Instrument and Control Engineers (SICE), pp. 2798-2803, SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007, Takamatsu, Japan, 07/9/17. https://doi.org/10.1109/SICE.2007.4421464

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title = "A design of the EEG feature detection and condition classification",

abstract = "In this paper, we classify the human conditions (before and after meal, before and after smoking) and extract the frequency feature of conditions by using the electroencephalograms (EEG). First, we measure the EEG data. Then, we classify the conditions by using the principal component analysis (PCA). Moreover, the EEG data is reconstructed by using the questionnaires and the result of classification. From the result, we consider ideal circumstance for the EEG measurement. Finally, the EEG data is decompressed to consider the EEG features of conditions. Then, in order to show the effectiveness of the proposed method, computer simulations are done.",

keywords = "Condition, EEG, PCA",

author = "Junko Murakami and Ito, {Shin Ichi} and Yasue Mitsukura and Jianting Cao and Minoru Fukumi",

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AU - Murakami, Junko

AU - Ito, Shin Ichi

AU - Mitsukura, Yasue

AU - Cao, Jianting

AU - Fukumi, Minoru

PY - 2007

Y1 - 2007

N2 - In this paper, we classify the human conditions (before and after meal, before and after smoking) and extract the frequency feature of conditions by using the electroencephalograms (EEG). First, we measure the EEG data. Then, we classify the conditions by using the principal component analysis (PCA). Moreover, the EEG data is reconstructed by using the questionnaires and the result of classification. From the result, we consider ideal circumstance for the EEG measurement. Finally, the EEG data is decompressed to consider the EEG features of conditions. Then, in order to show the effectiveness of the proposed method, computer simulations are done.

AB - In this paper, we classify the human conditions (before and after meal, before and after smoking) and extract the frequency feature of conditions by using the electroencephalograms (EEG). First, we measure the EEG data. Then, we classify the conditions by using the principal component analysis (PCA). Moreover, the EEG data is reconstructed by using the questionnaires and the result of classification. From the result, we consider ideal circumstance for the EEG measurement. Finally, the EEG data is decompressed to consider the EEG features of conditions. Then, in order to show the effectiveness of the proposed method, computer simulations are done.

KW - Condition

KW - EEG

KW - PCA

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T3 - Proceedings of the SICE Annual Conference

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A design of the EEG feature detection and condition classification

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出版物シリーズ

Other

ASJC Scopus subject areas

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