Title :
The Proposal of the EEG Characteristics Extraction Method in Weighted Principal Frequency Components Using the RGA
Author :
Ito, Shin-ichi ; Mitsukura, Yasue ; Miyamura, H.N. ; Saito, Takafumi ; Fukumi, Minoru
Author_Institution :
Dept. of Bio-Mech. & Intelligent Syst., Tokyo Univ. of Agric. & Technol.
Abstract :
An EEG has frequency components which can describe most of the significant features. These combinations are often unique like individual human beings and yet they have underlying basic features. These frequency components are contained the important and/or not so important components, and then each importance of these frequency components are different. The real-coded genetic algorithm (: RGA) is used for selecting and being weighted the principal characteristic frequency components. We attempt to construct mental change appearance model (: MCAM) of only one measurement point. In order to show the effectiveness of the proposed method, computer simulations are carried out by using real data
Keywords :
electroencephalography; genetic algorithms; medical signal processing; EEG characteristics extraction method; electroencephalography; mental change appearance model; real-coded genetic algorithm; weighted principal frequency component; Brain modeling; Data mining; Electroencephalography; Feature extraction; Frequency; Genetic algorithms; Humans; Intelligent systems; Proposals; Sensor phenomena and characterization; electroencephalogram; latency structure model; mental change; real-coded genetic algorithms (RGA);
Conference_Titel :
SICE-ICASE, 2006. International Joint Conference
Conference_Location :
Busan
Print_ISBN :
89-950038-4-7
Electronic_ISBN :
89-950038-5-5
DOI :
10.1109/SICE.2006.315293
