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

by Omar Al-ketbi, Marc Conrad
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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
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Index Terms

Computer Science
Information Sciences

Keywords

GMDH EEG BCI ANN Supervised ANN Unsupervised SOM

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