2D affective space model (ASM) for detecting autistic children

Razali, Najwani and Abdul Rahman, Abdul Wahab (2011) 2D affective space model (ASM) for detecting autistic children. In: 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE), 14-17 June 2011, Singapore.

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Abstract

There are many research works have been done on autism cases using brain imaging techniques. In this paper, the Electroencephalogram (EEG) was used to understand and analyze the functionality of the brain to identify or detect brain disorder for autism in term of motor imitation. Thus, the portability and affordability of the EEG equipment makes it a better choice in comparison with other brain imaging device such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and megnetoencephalography (MEG). Data collection consists of both autistic and normal children with the total of 6 children for each group. All subjects were asked to clinch their hand by following video stimuli which presented in 1 minute time. Gaussian mixture model was used as a method of feature extraction for analyzing the brain signals in frequency domain. Then, the extraction data were classified using multilayer perceptron (MLP). According to the verification result, the percentage of discriminating between both groups is up to 85% in average by using k-fold validation

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Additional Information:	6145/9657
Uncontrolled Keywords:	Electroencephalogram (EEG) , Gaussian Mixture Model (GMM) , Motor imitation , Multilayer perceptron(MLP)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Sis Zaleha Ibat
Date Deposited:	28 Feb 2012 12:59
Last Modified:	17 Dec 2020 00:09
URI:	http://irep.iium.edu.my/id/eprint/9657

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