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

Indu Sekhar Samant; Guru Kalyan Kanungo; Santosh Kumar Mishra

doi:https://doi.org/10.14445/22315381/IJETT-V3I6P207

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Volume 3 | Issue 6 | Year 2012 | Article Id. IJETT-V3I6P207 | DOI : https://doi.org/10.14445/22315381/IJETT-V3I6P207

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

Indu Sekhar Samant , Guru Kalyan Kanungo , Santosh Kumar Mishra

Citation :

Indu Sekhar Samant , Guru Kalyan Kanungo , Santosh Kumar Mishra, "Desired EEG Signals For Detecting Brain Tumor Using LMS Algorithm And Feedforward Network," International Journal of Engineering Trends and Technology (IJETT), vol. 3, no. 6, pp. 718-723, 2012. Crossref, https://doi.org/10.14445/22315381/IJETT-V3I6P207

Abstract

In Brain tumor diagno stic 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 signa l contains 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 signal are extracted using spectral estimation . Specifically , spectral analysis is achieved by using Fast Fourier Transform that extracts the signal features buried in a wide band of noise . The desired signal is undergone as training and testing of FLANN to effectively classify the EEG signal with Bra in tumor

Keywords

Brain Tumor ; CT ; EEG ; FLANN ; LMS .

References

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