Welcome to JJEM: A Multi-Disicplinary Journal of JNNCE, Shimoga
Rajashekhar U,  Neelappa,  Rashmi M. Hullamani,   Bhagyamma S,  Pavithra P S,  Choodarathnakara A L
The diagnosis system presents to classify the Electroencephalogram (EEG) brain signal of patient to distinguish between normal and abnormal which are tumor and epilepsy with better classification accuracy. To design automated classification of EEG signals for the detection of normal and abnormal activities using Wavelet transform and Artificial Neural Network (ANN) Classifier is considered. Here, the system uses the back propagation with feed forward for classification which follows the ANN classification with data set training. For training, the statistical principal features will be extracted with facilitate of data base samples. The test sample is going to be classified using ANN classifier parameters and its features. The system gives better performance accuracy for different test samples.
Artificial Neural Network; Brain-Computer Interface; Electroencephalogram; Support Vector Machine