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

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

2 Citations (Scopus)

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.

Original language	English
Title of host publication	SICE Annual Conference, SICE 2007
Publisher	Society of Instrument and Control Engineers (SICE)
Pages	2798-2803
Number of pages	6
ISBN (Print)	4907764286, 9784907764289
DOIs	https://doi.org/10.1109/SICE.2007.4421464
Publication status	Published - 2007
Externally published	Yes
Event	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan Duration: 2007 Sept 17 → 2007 Sept 20

Publication series

Name	Proceedings of the SICE Annual Conference

Other

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

Keywords

Condition
EEG
PCA

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/SICE.2007.4421464

Cite this

A design of the EEG feature detection and condition classification. / Murakami, Junko; Ito, Shin Ichi; Mitsukura, Yasue et al.

SICE Annual Conference, SICE 2007. Society of Instrument and Control Engineers (SICE), 2007. p. 2798-2803 4421464 (Proceedings of the SICE Annual Conference).

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

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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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.",

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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.

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KW - PCA

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A design of the EEG feature detection and condition classification

Abstract

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Keywords

ASJC Scopus subject areas

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