NDLI: Research on classification method of wavelet entropy and Fuzzy Neural Networks for motor imagery EEG

Content Provider	IEEE Xplore Digital Library
Author	Li, Xin Cui, Wei Li, Changwu
Copyright Year	2012
Description	Author affiliation: Institute of Electrical Engineering, Yanshan University, China (Li, Xin; Cui, Wei) \|\| Dalian Polytechnic University, China (Li, Changwu)
Abstract	Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics [1]. Here, new method based on wavelet entropy (WE) and Fuzzy Neural Networks (FNN) is applied. The WE carries information about the degree of order/disorder associated with a multi-frequency signal. In addition, the time evolution of the WE is calculated to give information about the dynamics in the EEG records. Within this framework, the major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals. This paper has tried to use fuzzy neural networks (FNN) as a classification method, which combines fuzzy membership and neural network frame, to drive the model automatically update in training and leaning processes, thus it can be applied in the simulations of complex conditions. In this way, EEG signals under motor imagery conditions were analyzed.
Starting Page	478
Ending Page	482
File Size	1112398
Page Count	5
File Format	PDF
ISBN	9781467315241
e-ISBN	9780956715715
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Huazhong Univ of Sci & Tec
Subject Keyword	Wavelet transforms Motor imagery EEG Wavelet entropy (WE) Fuzzy neural networks Brain modeling Feature extraction Wavelet analysis BCI Entropy Electroencephalography Fuzzy Neural Networks (FNN)
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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