NDLI: A method of mother wavelet function learning for DWT-based analysis using EEG signals

Content Provider	IEEE Xplore Digital Library
Author	Won-Seok Kang Kookrae Cho Seung-Hyun Lee
Copyright Year	2011
Description	Author affiliation: Division of Robot System, Daegu Gyeongbuk Institute of Science & Technology, Republic of Korea (Seung-Hyun Lee) \|\| Division of IT Convergence, Daegu Gyeongbuk Institute of Science & Technology, Republic of Korea (Won-Seok Kang; Kookrae Cho)
Abstract	In brain signals analysis, there are the supplementary devices such as EEG, fNIRS, MEG, fMRI, PET, etc. EEG is a popular secondary device due to the advantages of easy usability, mobility and low-cost. Many researchers have employed a Discrete Wavelet Transform (DWT) to classify EEG signals and make a clustering of the signal in brain-computer interface and medicine diagnosis. The precision of classification and clustering for EEG analysis depend on a mother wavelet. In order to improve the precision, the previous works has taken a hand-selection method to find out the best mother wavelet after simulation. It is necessary to improve the tested precision because the best mother wavelets for the acquired EEG signals are different depending on the subjects. In this paper, we suggest a novel approach which can select the best mother wavelets for DWT-based analysis in time-series sequences of EEG signals. To show the efficiency of the proposed method, we utilized a clustering method which can separate unsupervised EEG signals into the groups such as the ADHD (Attention Deficit Hyper-activity Disorder), the normal children, and the children in the boundary between ADHD and Normal children. As a result of simulation, we confirmed that the novel method improved the precision about 15% more than the previous.
Starting Page	1905
Ending Page	1908
File Size	1332486
Page Count	4
File Format	PDF
ISBN	9781424492909
ISSN	19300395
e-ISBN	9781424492893
DOI	10.1109/ICSENS.2011.6127405
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-28
Publisher Place	Ireland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Discrete wavelet transforms Pediatrics Artificial neural networks Wavelet analysis
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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