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Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks

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Main Authors: Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesa Pandiyan, Assoc. Prof., Nagarajan, Ramachandran, Prof. Dr., Sazali, Yaacob, Prof. Dr., Abdul Hamid, Adom, Prof. Madya
Other Authors: hema@unimap.edu.my
Format: Article
Language:English
Published: University of Basrah 2011
Subjects:
Brain machine interface
EEG signal processing
Recurrent neural networks
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/11346
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spelling my.unimap-113462011-03-21T08:39:32Z Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks Hema, Chengalvarayan Radhakrishnamurthy Paulraj, Murugesa Pandiyan, Assoc. Prof. Nagarajan, Ramachandran, Prof. Dr. Sazali, Yaacob, Prof. Dr. Abdul Hamid, Adom, Prof. Madya hema@unimap.edu.my Brain machine interface EEG signal processing Recurrent neural networks Link to publisher's homepage at http://www.uobasrah.edu.iq/ Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications. 2011-03-21T08:39:32Z 2011-03-21T08:39:32Z 2008 Article Iraqi Journal for Electrical and Electronic Engineering, vol.4(1), 2008, pages 77-85 1814-5892 (Print) 2078-6069 (Online) http://www.ijeee.org/volums/volume4/IJEEE4PDF/paper7.pdf http://hdl.handle.net/123456789/11346 en University of Basrah
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 Brain machine interface
EEG signal processing
Recurrent neural networks
spellingShingle Brain machine interface
EEG signal processing
Recurrent neural networks
Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesa Pandiyan, Assoc. Prof.
Nagarajan, Ramachandran, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Abdul Hamid, Adom, Prof. Madya
Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks
description Link to publisher's homepage at http://www.uobasrah.edu.iq/
author2 hema@unimap.edu.my
author_facet hema@unimap.edu.my
Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesa Pandiyan, Assoc. Prof.
Nagarajan, Ramachandran, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Abdul Hamid, Adom, Prof. Madya
format Article
author Hema, Chengalvarayan Radhakrishnamurthy
Paulraj, Murugesa Pandiyan, Assoc. Prof.
Nagarajan, Ramachandran, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Abdul Hamid, Adom, Prof. Madya
author_sort Hema, Chengalvarayan Radhakrishnamurthy
title Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks
title_short Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks
title_full Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks
title_fullStr Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks
title_full_unstemmed Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks
title_sort brain machine interface: analysis of segmented eeg signal classification using short-time pca and recurrent neural networks
publisher University of Basrah
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/11346
_version_ 1643790138044579840
score 13.214268

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