Open AccessExtraction of ElectroEncephaloGraph (EEG) Signal Using the Subband Coefficient of Wavelet Transform on Cursor Moves
Author(s) -
H Hindarto,
Arif Muntasa
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/532/1/012013
Subject(s) - electroencephalography , computer science , cursor (databases) , pattern recognition (psychology) , feature extraction , artificial intelligence , brain–computer interface , wavelet , backpropagation , wavelet transform , standard deviation , waveform , speech recognition , artificial neural network , statistics , mathematics , psychology , telecommunications , radar , psychiatry
This research explains the application of ElectroEncephaloGraph (EEG) signal waves used to move the up cursor and down cursor. In each sub band of the waveform, Electro Encephalo Graph (EEG) will produce the average and standard deviation to be used as a feature of the EEG. Artificial Neural Network Backpropagation as the basis for determining whether the cursor moves up or the cursor moves down. The data used in this study is EEG data derived from BCI Competition 2003 (BCI Competition 2003). Decision-making is done in two stages. In the first stage, the mean and standard deviation values on each wavelet subband as a feature extraction of EEG data. This feature is an input to the Backpropagation Neural Network. In the second stage of the process of inserting into two classes (class 0 and class 1) EEG data files, there are 260 EEG and 293 file training data files from EEG file test data files, totaling to 553 EEG data files. The results obtained for the classification of EEG results were 79.2%.