Desired EEG Signals for Detecting Brain Tumor Using LMS Algorithm and Feedforward Network
In Brain tumor diagnostic EEG is the most relevant in assessing 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 signal 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.