NDLI: EEG analysis using moving average-type neural network

Content Provider	IEEE Xplore Digital Library
Author	Murata, Toshihiro Akutagawa, Masatake Kaji, Yoshio Shichijou, Fumio Nagashino, Hirofumi Kinouchi, Yohsuke
Copyright Year	2008
Description	Author affiliation: Suzue Hospital, Sako Tokushima, 770 - 0028, Japan (Shichijou, Fumio) \|\| Faculty of Engineering, the University of Bunri, Sanuki Kagawa, 769 - 2193, Japan (Kaji, Yoshio) \|\| Faculty of Health Bioscience, the University of Tokushima, Kuramoto, 770 - 8503, Japan (Nagashino, Hirofumi) \|\| Faculty of Engineering, the University of Tokushima, Minamijosanjima, 770 - 8506 Japan (Murata, Toshihiro; Akutagawa, Masatake; Kinouchi, Yohsuke)
Abstract	The brain is one of nonlinear systems. EEG (electroencephalogram) is widely used to monitor the brain condition. Frequency analysis method is given as general EEG analysis method. DR analysis is to evaluate the deviation ratio of power spectrum of arbitrary frequency band. On the other hand, neural networks are used to analyze nonlinear systems because of their ability to capture the dynamics of complex system. In this study, we propose the application of moving average-type neural networks (MANN) to analyze the EEG and compare the result of MANN analysis with the one of DR analysis. In this time, we used the randomness to optimize the number of input units of MANNs. In MANN analysis method, the measured EEG is divided into overlapped period 20 seconds. A MANN is trained to predict the EEG using three previous samples in each period. After training, the connecting weights of each MANN are compared using the inner product to evaluate changes in brain condition. We used measured EEG in closing/opening eyes to analyze. According to the results, the usefulness of MANN analysis was confirmed. And MANN analysis has possibility to be able to apply to analysis of the other EEG data or the other nonlinear systems.
Starting Page	169
Ending Page	172
File Size	1101877
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4649117
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Neural network based EEG denoising

EEG signal classification method based on fractal features and neural network

Real-time automated neural-network sleep classifier using single channel EEG recording for detection of narcolepsy episodes

Classification of finger vibrotactile input using scalp EEG

Dynamic clustering for vigilance analysis based on EEG

Performance prediction using EEG and trial-invariant characteristic signals

Recognition of aging effect from cardiomechanical signals using novel SF-ART neural network

EEG classification of physiological conditions in 2D/3D environments using neural network

Detection of neonatal EEG seizure using multichannel matching pursuit

EEG analysis using moving average-type neural network

Similar Documents

Neural network based EEG denoising

EEG signal classification method based on fractal features and neural network

Real-time automated neural-network sleep classifier using single channel EEG recording for detection of narcolepsy episodes

Classification of finger vibrotactile input using scalp EEG

Dynamic clustering for vigilance analysis based on EEG

Performance prediction using EEG and trial-invariant characteristic signals

Recognition of aging effect from cardiomechanical signals using novel SF-ART neural network

EEG classification of physiological conditions in 2D/3D environments using neural network

Detection of neonatal EEG seizure using multichannel matching pursuit

EEG analysis using moving average-type neural network