CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Wavelet Energy based Neural Fuzzy Model for Automatic Motor Imagery Classification

by Girisha Garg, Shruti Suri, Rachit Garg, Vijander Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 7
Year of Publication: 2011
Authors: Girisha Garg, Shruti Suri, Rachit Garg, Vijander Singh
10.5120/3403-4745

Girisha Garg, Shruti Suri, Rachit Garg, Vijander Singh . Wavelet Energy based Neural Fuzzy Model for Automatic Motor Imagery Classification. International Journal of Computer Applications. 28, 7 ( August 2011), 1-7. DOI=10.5120/3403-4745

@article{ 10.5120/3403-4745,
author = { Girisha Garg, Shruti Suri, Rachit Garg, Vijander Singh },
title = { Wavelet Energy based Neural Fuzzy Model for Automatic Motor Imagery Classification },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 7 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number7/3403-4745/ },
doi = { 10.5120/3403-4745 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:14:06.419410+05:30
%A Girisha Garg
%A Shruti Suri
%A Rachit Garg
%A Vijander Singh
%T Wavelet Energy based Neural Fuzzy Model for Automatic Motor Imagery Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 7
%P 1-7
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain-computer interface (BCI) is a communication system by which a person can send messages without any use of peripheral nerves and muscles. BCI systems might help to restore abilities to patients who have lost sensory or motor function because of the damaged region, such as amyotrophic lateral sclerosis (ALS), spinal cord injury, brainstem stroke, or quadriplegic patients. Brain computer interfacing can be effectively implemented by analyzing EEG signals generated in the brain. This paper presents a method for accurately classifying EEG signals generated by imagery left and right hand movements. Firstly, wavelet transform and energy of the decomposed signal is used to obtain the final feature vector matrix. Secondly, the feature data is classified using ANFIS. . The Mutual Information value calculated is 1.2942 bit. The classification accuracy achieved 93.5% in the course of testing on the data from subject. Support Vector Machine is also used to compare the performance with ANFIS.

References
  1. Xu H., Lou J., Su R., Zhang E.,2009 "Feature extraction and classification of EEG for imaging left-right hands movement," iccsit,2nd IEEE International Conference on Computer Science and Information Technology.
  2. Qiao X., Wang Y., Li D., and Tian L.,2010 "Feature extraction and classifier evaluation of EEG for imaginary hand movements", in Proc. ICNC.
  3. Zou L. , Guodong X.W., Ma S.Z.,2010 “EEG Feature extraction and pattern classification based on Motor Imagery in Brain-computer Interface”, ICCI, 9th IEEE International Conference.
  4. Hu J. F., Bao X. C., and Mu Z.D.,2008 “Classification of Motor Imagery EEG Based on Phase Synchronization,” Microelectronics and Computer.
  5. Hu J. F., Mu Z.D., and Xiao D.,2008 “Classification of motor imagery EEG signals based on energy entropy,” Computer Engineering and Applications.
  6. Ramoser H., Muller-Gerking J., and Pfurtscheller G., “Optimal spatial filtering of single trial EEG during imagined hand movement”, IEEE Trans. Rehabil. Eng.,
  7. Institute for Biomedical Engineering, Graz University of Technology,http://www.bbci.de/competition/ii/.
  8. Zhang L., Liu G., and Wu Y.,2010“Wavelet and Common Spatial Pattern for EEG Signal Feature Extraction and Classification,” 201O International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE)
  9. Schlögl A., Kronegg Julien,HugginsE. Jane,Mason G. Steve, 2007 Evaluation Criteria for BCI Research. In Toward Brain-computer Interfacing.
  10. Sanej, Saeid and Chambers, J. A. EEG Signal PRocessing. WILEY, 2007.
  11. Ungureanu, M., Bigan, C. and Strungaru, R. 2, 2004, Independent Component Analysis Applied in Biomedical Signal Processing Measurement Science Review, Vol. 4, pp. 1-8.
  12. Tutorials, SVM. www.svms.org/tutorials/. www.svms.org.
  13. Online.
  14. Srinivasan, V., Eswaran, C. and Sriram, N. 3, 2007, Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks. Information Technology in Biomedicine, Vol. 11, pp. 288-295.
  15. G. Garg, V. Singh, J. R. P Gupta, A. P. Mittal, S. Chandra, “Computer Assisted Automatic Sleep Scoring System Using Relative Wavelet Energy Based Neuro Fuzzy Model” WSEAS Transactions on Biology and Biomedicine, Vol. 8, 2011.
  16. G. Garg, V. Singh, J.R.P Gupta A.P. Mittal, “Relative Wavelet Energy as a New Feature Extractor For Sleep Classification using EEG Signals” Accepted for publication in Journal of Biomedical Signal Processing. Jan- June Issue, 2011.
  17. Krausz G, Ortner R, Opisso, “Accuracy of a Brain Computer Interface (P300 Spelling Device) used by People with Motor Impairments” US National Library of MedicineNational Institutes of Health, 2011.
  18. Faradji F, Ward RK, Birch GE., “Toward development of a two-state brain-computer interface based on mental tasks” US National Library of MedicineNational Institutes of Health, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

ANFIS EEG signals SVM wavelet transform motor imagery

Powered by PhDFocusTM