EEG Signal Verification Using Supervised Neural Network - C. Jeyalakshmi, G. Subhasri, A. Revathi (International Journal of Control and Automation, 2020)
In order to depict the EEG signal utilizing regulated neural system, this framework proposes a programmed supportive network for grouping by using back propagation neural system for medicinal application such as brain tumor and epilepsy identification. The location of the cerebrum tumor is a difficult issue, because of the structure of the tumor cells. The manual examination of the brain sign calculation is tedious, inaccurate and requires concentrated prepared individual to stay away from analytic mistakes. And hence the proposed work describes in which the back propagation neural system is utilized to arrange the brain EEG signals that whether it is tumor case or epilepsy case or ordinary. Back Propagation Network with image as well as data preparing methods was utilized to execute an automated Brain Tumor classification. Here the decision making can be done in two states: Extraction of features by Principal Component Analysis and the grouping using Back Propagation Neural Network (BPNN). The evaluation of BPNN classifier performance can be done by training and accurate classification. This promising tool provides fast and accurate in case of classifying the signals when compared to other neural network systems even in characterization of the tumors.