Extraction of ElectroEncephaloGraph (EEG) Signal Using the Subband Coefficient of Wavelet Transform on Cursor Moves

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...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2019-05, Vol.532 (1), p.12013
Main Authors: Hindarto, H, Muntasa, A
Format: Article
Language:English
Subjects:
Artificial neural networks
Back propagation
Back propagation networks
BackPropagation
Competition
Decision making
Device Standart
EEG
Electroencephalography
Feature extraction
Mean
Neural networks
Standard deviation
Waveforms
Wavelet
Wavelet transforms
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Summary: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%.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/532/1/012013