Paper Title
EEG Signal Classification Using Soft Computing Techniques For Brain Disease Diagnosis

-The project proposes an automatic support system for tumor classification using the soft computing techniques. The detection of the brain tumor is a challenging problem, due to the structure of the tumor cells. The artificial neural network is used to classify the stage of brain EEG signal that if it is the case of tumor or epilepsy or normal. The manual analysis of the signal is time consuming, inaccurate and requires intensive trained person to avoid diagnostic errors. The soft computing techniques are employed for the classification of the EEG signals as the techniques are intended to model and make possible solutions to real world tribulations. The probability of correct classification has been increased by using soft computing techniques like Principal Component Analysis with neural network and Fuzzy Logic. Back Propagation Network with image and data processing techniques is employed to implement an automated Brain Tumor classification. Decision making is performed in two stages: feature extraction using Principal Component Analysis and the classification using Back Propagation Network (BPN). The performance of the BPN classifier was evaluated in terms of training performance and classification accuracies. Back Propagation Network gives fast and accurate classification than other neural networks and it is a promising tool for classification of the Tumors.