Abstract
In this research, it localized structural feature selection method has been used as a base of quantifying structural changes with time for Electroencephalograms (EEG) obtained from four states two patient and two healthy with eyes open and eyes closed in both. Then these structural characteristics have been submitted to the back propagation neural network for the purpose of signal distinction by the intelligent methods. BFGS Quasi-Newton Back propagation function has been used with the data of the network. It gives good results at testing to the values of features extractions that they have not been training with, and it has been reached to the goal with minimum iteration from other common function that is used with back propagation neural network.
The results for classifying EEG using back propagation neural network show that Alzheimer sick can be detected hardly 100% in many channels in case in taken EEG for the patient with eyes closed. The transformed inputs (from the original data of the signal to the features intentional in the research) are ideally suited for effective classification of EEG data. Recognition rates vary for each EEG channel data between 50-100% correct recognition in the four cases. The follow up method can be useful in several applications including time-series analysis, signal processing and speech recognition.
