Identification Of Electrode Enchepalo Graph Signal To Move The Cursor Using Back Propagation Method

Abstract

In this paper the researchers describe the application of back propagation neural networks as classification and sampling technique (TS) for feature extraction of waveform signals Electro Enchepalo Graph (EEG). This study aims to develop a system that can recognize the EEG signals are used to move the cursor. In this study used 90 EEG signal data files for the training process. In the identification process into four, grade, used 150 files EEG signal data. The results obtained for the classification of this signal is 80% of the 150 files were tested on the data signal mapping process. Keywords: sampling techniques, neural networks, EEG signals