EEG Spectrogram Classification Employing ANN for IQ Application

Mahfuzah, Mustafa and Mohd Nasir, Taib and Sahrim, Lias and Zunairah, Murat and Norizam, Sulaiman (2013) EEG Spectrogram Classification Employing ANN for IQ Application. In: Technological Advances In Electrical, Electronics And Computer Engineering (TAEECE), 9-11 May 2013 , Konya. pp. 199-203..

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The term intelligence is associated in many areas such as linguistic, mathematical, music and art. In this paper, Intelligence Quotient (IQ) is measured using Electroencephalogram (EEG) from the human brain. The EEG signals are then used to form the spectrogram images, from which a large data of Gray Level Co-occurrence Matrix (GLCM) texture features were extracted. Then, Principal Component Analysis (PCA) is used to reduce the big matrix, and is followed with the classification of the EEG spectrogram image in IQ application using ANN algorithm. The results are then validated based on the concept of Raven's Standard Progressive Matrices (RPM) IQ test. The results showed that the ANN is able to classify the EEG spectrogram image with 88.89% accuracy and 0.0633 MSE.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: ANN; GLCM; STFT; EEG signals
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 21 Aug 2014 07:39
Last Modified: 11 Apr 2018 01:55
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