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

Title
A method of mother wavelet function learning for DWT-based analysis using EEG signals
Authors
Kang, Won-SeokCho, KookraeLee, Seung-Hyun
DGIST Authors
Kang, Won-SeokCho, Kookrae
Issue Date
2011
Citation
10th IEEE SENSORS Conference 2011, SENSORS 2011, 1905-1908
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
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. © 2011 IEEE.
URI
http://hdl.handle.net/20.500.11750/3921
DOI
10.1109/ICSENS.2011.6127405
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
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Collection:
Division of IoT∙Robotics Convergence Research2. Conference Papers
Companion Diagnostics and Medical Technology Research Group2. Conference Papers


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