Cover Image

EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces

Link to publisher's homepage at http://www.amse-modeling.com

Saved in:
Bibliographic Details
Main Authors: Paulraj, Murugesapandian, Hema, Chengalvarayan Radhakrishnamurthy, Ramachandran, Nagarajan, Sazali, Yaacob, Abdul Hamid, Adom
Other Authors: paul@unimap.edu.my
Format: Article
Language:English
Published: Association for the Advancement of Modelling & Simulation Techniques in Enterprises (A.M.S.E.) 2009
Subjects:
EEG signal processing
Mental tasks
Principal component analysis
PSO neural networks
Brain machine interfaces
Medical computing
Electroencephalography
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7414
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-7414
record_format dspace
spelling my.unimap-74142009-12-14T06:39:51Z EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces Paulraj, Murugesapandian Hema, Chengalvarayan Radhakrishnamurthy Ramachandran, Nagarajan Sazali, Yaacob Abdul Hamid, Adom paul@unimap.edu.my EEG signal processing Mental tasks Principal component analysis PSO neural networks Brain machine interfaces Medical computing Electroencephalography Link to publisher's homepage at http://www.amse-modeling.com The brain uses the neuromuscular channels to communicate and control its external environment, however many disorders can disrupt these channels. Amyotrophic lateral sclerosis is one such disorder which impairs the neural pathways and completely paralyses the patient. Rehabilitation of such patients is possible through a brain machine interface which provides a direct communication pathway between the brain and an external device. Brain machine interfaces (BMI) are designed using the electrical activity of the brain detected by scalp Electroencephalogram (EEG) electrodes. In this paper a novel training algorithm using Particle Swarm Optimization (PSO) is proposed, the results are compared with the classical Back Propagation (BP) training algorithm, Feed Forward Neural Network (FFNN) architecture with one hidden layer is used in this study. Five mental tasks signals acquired from two subjects were studied; a combination of two tasks is used for classification. Short time principal component analysis is used to extract the features. The features are used for training and testing the neural network. Classifications of 10 different task combinations were studied for two subjects. Improved classification performance was achieved using the PSO algorithm in comparison to the B.P. Algorithm. Average classification accuracies obtained with the PSO FFNN vary from 81.5 % to 97.5 %. 2009-12-14T06:39:50Z 2009-12-14T06:39:50Z 2008 Article Modelling, Measurement and Control C, vol.69 (2), 2008, pages 20-33. http://www.amse-modeling.com/ind2.php?cont=03per&menu=/menu3.php&pag=/articslist.php&vis=1&buscarart=1&id_ser=2C http://hdl.handle.net/123456789/7414 en Association for the Advancement of Modelling & Simulation Techniques in Enterprises (A.M.S.E.)
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 EEG signal processing
Mental tasks
Principal component analysis
PSO neural networks
Brain machine interfaces
Medical computing
Electroencephalography
spellingShingle EEG signal processing
Mental tasks
Principal component analysis
PSO neural networks
Brain machine interfaces
Medical computing
Electroencephalography
Paulraj, Murugesapandian
Hema, Chengalvarayan Radhakrishnamurthy
Ramachandran, Nagarajan
Sazali, Yaacob
Abdul Hamid, Adom
EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces
description Link to publisher's homepage at http://www.amse-modeling.com
author2 paul@unimap.edu.my
author_facet paul@unimap.edu.my
Paulraj, Murugesapandian
Hema, Chengalvarayan Radhakrishnamurthy
Ramachandran, Nagarajan
Sazali, Yaacob
Abdul Hamid, Adom
format Article
author Paulraj, Murugesapandian
Hema, Chengalvarayan Radhakrishnamurthy
Ramachandran, Nagarajan
Sazali, Yaacob
Abdul Hamid, Adom
author_sort Paulraj, Murugesapandian
title EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces
title_short EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces
title_full EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces
title_fullStr EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces
title_full_unstemmed EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces
title_sort eeg signal classification using particle swarm optimization (pso) neural network for brain machine interfaces
publisher Association for the Advancement of Modelling & Simulation Techniques in Enterprises (A.M.S.E.)
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7414
_version_ 1643788807572553728
score 13.214268

Similar Items