Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?
Affiliation
University of BedfordshireIssue Date
2013Subjects
electroencephalogramEEG
brain-computer interface
BCI
data classification
GMDH
ANN
supervised ANN
Unsupervised SOM
pattern recognition
data mining
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Show full item recordAbstract
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.Citation
al-Ketbi, O., Conrad, M. (2013) 'Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do better?', International Journal of Computer Applications 74 (4):37Publisher
IJCA JournalAdditional Links
http://research.ijcaonline.org/volume74/number4/pxc3889901.pdfType
ArticleLanguage
enISSN
0975-8887ae974a485f413a2113503eed53cd6c53
10.5120/12876-9901
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