Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do better?
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10.5120/12876-9901 |
Omar Al-ketbi and Marc Conrad. Article: Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do betterh. International Journal of Computer Applications 74(4):37-44, July 2013. Full text available. BibTeX
@article{key:article,
author = {Omar Al-ketbi and Marc Conrad},
title = {Article: Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do betterh},
journal = {International Journal of Computer Applications},
year = {2013},
volume = {74},
number = {4},
pages = {37-44},
month = {July},
note = {Full text available}
}
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
- 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.
- 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)
- 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
- Gaidos, S. 2011. Mind Controlled, an article in Science News Magazine, pp. 26-29, 2nd July
- Krauledat, M. 2008. Analysis of Nonstationarities in EEG Signals for Improving Brain-Computer Interface Performance, Doctoral Dissertation, Technical University Berlin
- 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
- Moazzami, M. M. and Mutka, M. 2011. EEG signal Processing in Brain-Computer Interface, Master Thesis, Computer Science Department, Michigan State University, September
- 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
- Bogue, R. 2010. Brain-Computer Interfaces: Control by Thought", Industrial Robot: An International Journal, Vol. 37 Issue 2, pp. 126-132
- Friston K. J. and Dolan R. J. 2010. Computational and Dynamic Models in Neuroimaging, Neuroimage 52, pp. 752-765
- Jabr, F. 2011. Mind-Controlled Prosthetics to Help Amputees, New Scientist Magazine, issue 2810, 28 April
- 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
- 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
- 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
- Adlakha, A. 2002. Single Trial EEG Classification, Swiss Federal Institute of Technology
- 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