Classification of EEG spectrogram image with ANN approach for brainwave balancing application

Mahfuzah, Mustafa and Mohd Nasir, Taib and Zunairah, Murat and Norizam, Sulaiman and Siti Armiza, Mohd Aris (2011) Classification of EEG spectrogram image with ANN approach for brainwave balancing application. Classification of EEG Spectrogram Image. pp. 30-37. ISSN 1473-804x(Online); 1473-8031(print) . (Published)

[img] PDF
Classification_of_EEG_Spectrogram_Image_with_ANN_approach_for_Brainwave_Balancing_Application.pdf - Published Version
Restricted to Repository staff only

Download (234kB) | Request a copy


In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis(PCA) is used to reduce the feature dimension. The result shows that the proposed model is able to classify EEG spectrogram images with 77% to 84% accuracy for three classes of brainwave balancing application with an optimized ANN model in training by varying the neurons in the hidden layer, epoch, momentum rate and learning rate.

Item Type: Article
Uncontrolled Keywords: EEG spectrogram image; Artificial Neural Network (ANN; Principal Component Analysis(PCA)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 06 Aug 2015 07:54
Last Modified: 29 Aug 2018 02:27
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item