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Fast Detection of Brain Disorders using EEG Signal


Affiliations
1 Department of Computer Science, University of Sydney, Australia
2 Department of Computer Science, University of Sydney, Australia
3 Department of Computer Science, University of Sydney, Australia, India
     

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EEG is brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. The analysis of brain waves plays an important role in diagnosis of different brain disorders. The electroencephalogram (EEG) is the recording of electrical activity of the brain; it is a very effective tool for understanding the complex dynamical behavior of the brain. EEG signal towards the detection of abnormalities follows mainly four stages: pre-processing, feature extraction, feature selection, classification and ischemic episode recognition. The performance of detection shows better accuracy than the existing.


Keywords

Electroencephalogram, Pre-Processing, Feature Selection, Feature Extraction, Classification.
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  • Fast Detection of Brain Disorders using EEG Signal

Abstract Views: 37  |  PDF Views: 3

Authors

M. S. Jamuar
Department of Computer Science, University of Sydney, Australia
A. Shah
Department of Computer Science, University of Sydney, Australia
M. Kucic
Department of Computer Science, University of Sydney, Australia, India

Abstract


EEG is brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. The analysis of brain waves plays an important role in diagnosis of different brain disorders. The electroencephalogram (EEG) is the recording of electrical activity of the brain; it is a very effective tool for understanding the complex dynamical behavior of the brain. EEG signal towards the detection of abnormalities follows mainly four stages: pre-processing, feature extraction, feature selection, classification and ischemic episode recognition. The performance of detection shows better accuracy than the existing.


Keywords


Electroencephalogram, Pre-Processing, Feature Selection, Feature Extraction, Classification.