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Desired EEG Signals For Detecting Brain Tumor Using LMS Algorithm And Feedforward Network

Author(s): Indu Sekhar Samant#1, Guru Kalyan Kanungo#2 , Santosh Kumar Mishra*3

Journal: International Journal of Engineering Trends and Technology
ISSN 2231-5381

Volume: 3;
Issue: 6;
Start page: 718;
Date: 2012;
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Keywords: Brain Tumor | CT | EEG | FLANN | LMS.

In Brain tumor diagnostic EEG is the most relevant in assesing how basic functionality is affected by the lesion.EEG continues to be an attractive tool in clinical practice due to its non invasiveness and real time depication of brain function. But the EEG signalcontains the useful information along with redundant or noise information. In this Paper Least Mean Square algorithm is used to remove the artifact in the EEG signal. , generic features present in the EEG signalare extracted using spectral estimation. Specifically, spectral analysis is achieved by using Fast FourierTransform that extracts the signal features buried in awide band of noise. The desired signal is undergone as training and testing of FLANN to effectively classify the EEG signal with Brain tumor.
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