EEG Discrimination of Rats under Different Architectural Environments using ANNs

The present work introduces a new method for discriminating electroencephalogram (EEG) power spectra of rat's brain housed in different architectural shapes. The ability of neural networks for discrimination is used to describe the effect of different environments on brain activity. In this res...

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Bibliographic Details
Published in:International journal of computer science and information security 2015-12, Vol.13 (12), p.24-24
Main Authors: El-Gohary, Mohamed I, Al-Zohairy, Tamer A A, Eissa, Amir M M, Eldeghaidy, Sally M, El-Hafez, Hussein M H Abd
Format: Article
Language:English
Subjects:
Algorithms
Brain
Electroencephalography
Multilayers
Networks
Neural networks
Power spectra
Rats
Online Access:Get full text
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Summary:The present work introduces a new method for discriminating electroencephalogram (EEG) power spectra of rat's brain housed in different architectural shapes. The ability of neural networks for discrimination is used to describe the effect of different environments on brain activity. In this research the rats were divided into four groups according to the type of environmental shapes as: control (normal cage), pyramidal, inverted pyramidal and circular. The brain activities (EEG) were recorded from rats of each group. Fast Fourier Transform analysis of EEG signals was carried out to obtain power spectra. Two different neural networks are used as classifiers for power spectra of the different 4 groups: multi-layer perceptron with backpropagation and radial basis function RBF networks with unsupervised K means clustering algorithm. Experimental studies have shown that the proposed algorithms give good results when applied and tested on the four groups. The multilayer with backpropagation and radial basis function networks achieved a performance rate reaching 94.4% and 96.67% respectively.
ISSN:1947-5500