CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 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

Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do betterh

by Omar Al-ketbi, Marc Conrad
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 4
Year of Publication: 2013
Authors: Omar Al-ketbi, Marc Conrad
10.5120/12876-9901

Omar Al-ketbi, Marc Conrad . Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do betterh. International Journal of Computer Applications. 74, 4 ( July 2013), 37-44. DOI=10.5120/12876-9901

@article{ 10.5120/12876-9901,
author = { Omar Al-ketbi, Marc Conrad },
title = { Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do betterh },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 4 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 37-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number4/12876-9901/ },
doi = { 10.5120/12876-9901 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:21.866235+05:30
%A Omar Al-ketbi
%A Marc Conrad
%T Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do betterh
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 4
%P 37-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back-prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i. e. noise and accuracy) classify a given set of BCI's EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI's EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose.

References
  1. Bos, D. P. O. , Reuderink, B. , van de Laar, B. , Gürkök, H. , Mühl, C. , Poel, M. , Nijholt, A. & Heylen, D. 2010. Brain-computer interfacing and games. In Brain-Computer Interfaces (pp. 149-178). Springer London.
  2. Fetaji, M. , Loskoska, S. , Fetaji, B. and Ebibi, M. 2007. Investigating Human Computer Interaction Issues in Designing Efficient Virtual Learning Environments, Balkan Conference in Informatics (BCI 2007)
  3. Nijboer, F. , Sellers, E. , Mellinger J. , Jordan M. , Matuz, T. , Furdea, A. , Halder, S. , Mochty, U. , Krusienski, D. and Vaughan, T. 2008. A P300-Based Brain-Computer Interface for People With Amyotrophic Lateral Sclerosis, Clin. Neurophysiol
  4. Gaidos, S. 2011. Mind Controlled, an article in Science News Magazine, pp. 26-29, 2nd July
  5. Krauledat, M. 2008. Analysis of Nonstationarities in EEG Signals for Improving Brain-Computer Interface Performance, Doctoral Dissertation, Technical University Berlin
  6. Huan, N. J. and Palaniappan, R. 2004. Classification of Mental Tasks using Fixed and Adaptive Autoregressive Models of EEG Signals, pp. 507-510, IEEE EMBS Conference
  7. Moazzami, M. M. and Mutka, M. 2011. EEG signal Processing in Brain-Computer Interface, Master Thesis, Computer Science Department, Michigan State University, September
  8. Wolpaw, J. R. , Birbaumer, N. , McFarland, D. J. , Pfurtscheller, G. and Vaughan, T. M. 2002. Brain-Computer Interfaces for Communication and Control, Clin. Neurophysiology, vol. 113, pp. 767–791
  9. Bogue, R. 2010. Brain-Computer Interfaces: Control by Thought", Industrial Robot: An International Journal, Vol. 37 Issue 2, pp. 126-132
  10. Friston K. J. and Dolan R. J. 2010. Computational and Dynamic Models in Neuroimaging, Neuroimage 52, pp. 752-765
  11. Jabr, F. 2011. Mind-Controlled Prosthetics to Help Amputees, New Scientist Magazine, issue 2810, 28 April
  12. Abdel-Aal, R. E. 2005. Improved Classification of Medical Data Using Abductive Network Committees Trained on Different Feature Subsets, Computer Methods and Programs in Biomedicine, Vol. 80, pp. 141-153
  13. Schetinin, V. 2001. Polynomial Neural Networks Learnt to Classify EEG Signals, NIMIA-SC2001 - NATO Advanced Study Institute on Neural Networks for Instrumentation, Measurement, and Related Industrial Applications: Study Cases, Crema, Italy, 9-20 October
  14. Chumerin, N. , Manyakov, N. , Combaz, A. and Van Hulle, M. 2009. An Application of Feature Selection to On-Line P300 Detection in Brain-Computer Interface. IEEE International Workshop on Machine Learning for Signal Processing a16
  15. Adlakha, A. 2002. Single Trial EEG Classification, Swiss Federal Institute of Technology
  16. Lotte, F. , Congedo, M. , Lecuyer, A. and Arnaldi, B. (2007): A Review of Classification Algorithms for EEG-Based Brain-Computer Interfaces, J Neural Eng 4: R1-R13
Index Terms

Computer Science
Information Sciences

Keywords

GMDH EEG BCI ANN Supervised ANN Unsupervised SOM

Powered by PhDFocusTM