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Transactions: INTERNATIONAL JOURNAL of COMPUTERS
Transactions ID Number: 19-436
Full Name: Yuji Mizuno
Position: Student
Age: ON
Sex: Male
Address: 152-52, Sugo, Takizawa, Iwate, 020-0193
Country: JAPAN
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E-mail address: g231h030@s.iwate-pu.ac.jp
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Title of the Paper: Clustering of EEG data using maximum entropy method and LVQ
Authors as they appear in the Paper: Yuji Mizuno, Hiroshi Mabuchi, Goutam Chakraborty and Masafumi Matsuhara
Email addresses of all the authors: g231h030@s.iwate-pu.ac.jp,mabu@soft.iwate-pu.ac.jp,goutam@soft.iwate-pu.ac.jp,masafumi@soft.iwate-pu.ac.jp
Number of paper pages: 8
Abstract: The study of extracting electroencephalogram (EEG) data as a source of significant information has recently gained attention. However, since EEG data are complex, it is difficult to extract them as a source of intended, significant information. In order to effectively extract EEG data, this paper employs the maximum entropy method (MEM) for frequency analyses and investigates an alpha frequency band and beta frequency band in which features are more apparent. At this time, both the alpha and beta frequency bands are divided further into several sub-bands so as to extract detailed EEG data where the loss of data is small. In addition, learning vector quantization (LVQ) is used for clustering the EEG data with features extracted. In this paper, we will demonstrate the effectiveness of the proposed method by applying it to the EEG data of one subject and two subjects and comparing the results with other related studies. By applying the proposed method further to the!
EEG data of three subjects, and comparing the results with a related study, the effectiveness of the proposed method will be determined.
Keywords: Brain-Computer Interface(BCI), Clustering, EEG, LVQ, Maximum Mntropy Method(MEM)
EXTENSION of the file: .doc
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