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Recognition of motor imagery of hand movements for a BMI using PCA features

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Main Authors: Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Sazali, Yaacob, Abd Hamid, Adom, Ramachandran, Nagarajan
Other Authors: hema@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2009
Subjects:
Biology computing
Brain-computer interfaces
Electroencephalography
Medical image processing
Neuromuscular stimulation
Brain machine interfaces
EEG
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7394
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spelling my.unimap-73942009-12-11T04:12:10Z Recognition of motor imagery of hand movements for a BMI using PCA features Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesapandian Sazali, Yaacob Abd Hamid, Adom Ramachandran, Nagarajan hema@unimap.edu.my Biology computing Brain-computer interfaces Electroencephalography Medical image processing Neuromuscular stimulation Brain machine interfaces EEG Link to publisher's homepage at http://ieeexplore.ieee.org Motor imagery is the mental simulation of a motor act that includes preparation for movement and mental operations of motor representations implicitly or explicitly. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through a brain machine interfaces (BMI). In other words a BMI can be used to rehabilitate people suffering from neuromuscular disorders as a means of communication or control. This paper presents a novel approach in the design of a four state BMI using two electrodes. The BMI is designed using Neural Network Classifiers. The performance of the BMI is evaluated using two network architectures. The performance of the proposed algorithm has an average classification efficiency of 93.5%. 2009-12-10T03:45:53Z 2009-12-10T03:45:53Z 2008-12-01 Working Paper p.1-4 978-1-4244-2315-6 http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4786683 http://hdl.handle.net/123456789/7394 en Proceedings of the International Conference on Electronic Design (ICED 2008) Institute of Electrical and Electronics Engineering (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 Biology computing
Brain-computer interfaces
Electroencephalography
Medical image processing
Neuromuscular stimulation
Brain machine interfaces
EEG
spellingShingle Biology computing
Brain-computer interfaces
Electroencephalography
Medical image processing
Neuromuscular stimulation
Brain machine interfaces
EEG
Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesapandian
Sazali, Yaacob
Abd Hamid, Adom
Ramachandran, Nagarajan
Recognition of motor imagery of hand movements for a BMI using PCA features
description Link to publisher's homepage at http://ieeexplore.ieee.org
author2 hema@unimap.edu.my
author_facet hema@unimap.edu.my
Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesapandian
Sazali, Yaacob
Abd Hamid, Adom
Ramachandran, Nagarajan
format Working Paper
author Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesapandian
Sazali, Yaacob
Abd Hamid, Adom
Ramachandran, Nagarajan
author_sort Hema, Chengalvarayan Radhakrishnamurthy
title Recognition of motor imagery of hand movements for a BMI using PCA features
title_short Recognition of motor imagery of hand movements for a BMI using PCA features
title_full Recognition of motor imagery of hand movements for a BMI using PCA features
title_fullStr Recognition of motor imagery of hand movements for a BMI using PCA features
title_full_unstemmed Recognition of motor imagery of hand movements for a BMI using PCA features
title_sort recognition of motor imagery of hand movements for a bmi using pca features
publisher Institute of Electrical and Electronics Engineering (IEEE)
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7394
_version_ 1643788802643197952
score 13.214268

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