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Improving classification of EEG signals for a four-state brain machine interface

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Bibliographic Details
Main Authors: Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr., Abdul Hamid, Adom, Prof. Dr.
Other Authors: hemacr@karpagam.ac.in
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2013
Subjects:
Band power
Brain machine interfaces
Dynamic neural networks
Neural networks
Parseval theorem
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/27029
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spelling my.unimap-270292013-07-23T14:57:22Z Improving classification of EEG signals for a four-state brain machine interface Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr. Abdul Hamid, Adom, Prof. Dr. hemacr@karpagam.ac.in Band power Brain machine interfaces Dynamic neural networks Neural networks Parseval theorem Link to publisher's homepage at http://ieeexplore.ieee.org/ Neural network classifiers are one among the popular modes in the design of classifiers for electroencephalograph based brain machine interfaces. This study presents algorithms to improve the classification performance of motor imagery for a four state brain machine interface. Dynamic neural network models with band power and Parseval energy density features are proposed to improve the classification of task signals. Motor imagery signals recorded noninvasively at the sensorimotor cortex region using two bipolar electrodes are used in the study. The performances of the proposed algorithms are compared with a static neural classifier. Average classification performance of 97.7% was achievable. Experiment results show that the distributed time delay neural network model out performs the layered recurrent and feed forward neural classifiers 2013-07-23T14:57:22Z 2013-07-23T14:57:22Z 2012 Working Paper p. 615-620 978-146731666-8 http://hdl.handle.net/123456789/27029 en Proceedings of the 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) 2012 Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Band power
Brain machine interfaces
Dynamic neural networks
Neural networks
Parseval theorem
spellingShingle Band power
Brain machine interfaces
Dynamic neural networks
Neural networks
Parseval theorem
Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
Abdul Hamid, Adom, Prof. Dr.
Improving classification of EEG signals for a four-state brain machine interface
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 hemacr@karpagam.ac.in
author_facet hemacr@karpagam.ac.in
Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
Abdul Hamid, Adom, Prof. Dr.
format Working Paper
author Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
Abdul Hamid, Adom, Prof. Dr.
author_sort Hema, Chengalvarayan Radhakrishnamurthy
title Improving classification of EEG signals for a four-state brain machine interface
title_short Improving classification of EEG signals for a four-state brain machine interface
title_full Improving classification of EEG signals for a four-state brain machine interface
title_fullStr Improving classification of EEG signals for a four-state brain machine interface
title_full_unstemmed Improving classification of EEG signals for a four-state brain machine interface
title_sort improving classification of eeg signals for a four-state brain machine interface
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2013
url http://dspace.unimap.edu.my/xmlui/handle/123456789/27029
_version_ 1643795131350908928
score 13.222552

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