@conference{Razali2011536,
title = {2D Affective Space Model (ASM) for detecting autistic children},
author = {N Razali and A Wahab},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052392399&doi=10.1109%2fISCE.2011.5973888&partnerID=40&md5=f6ea401148e6558b861e4df6407e527e},
doi = {10.1109/ISCE.2011.5973888},
isbn = {9781612848433},
year = {2011},
date = {2011-01-01},
journal = {Proceedings of the International Symposium on Consumer Electronics, ISCE},
pages = {536-541},
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. © 2011 IEEE.},
note = {cited By 8},
keywords = {Autistic Children, Brain Disorders, Brain Imaging, Brain Imaging Techniques, Brain Signals, Children with Autism, Consumer Electronics, Data Collection, Diseases, Electroencephalogram, Electroencephalography, Feature Extraction, Frequency Domains, Functional Magnetic Resonance Imaging, Gaussian Mixture Model, Magnetic Resonance Imaging, Multi Layer Perceptron, Multilayer Perceptron, Multilayers, Positron Emission Tomography, Resonance, Space Models, Verification Results},
pubstate = {published},
tppubtype = {conference}
}

@conference{KhazaalShams2011653,
title = {Characterizing autistic disorder based on principle component analysis},
author = {W Khazaal Shams and A W Abdul Rahman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855644760&doi=10.1109%2fISIEA.2011.6108797&partnerID=40&md5=c486566e2d7ff404d830704c0b404067},
doi = {10.1109/ISIEA.2011.6108797},
isbn = {9781457714184},
year = {2011},
date = {2011-01-01},
journal = {2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011},
pages = {653-657},
abstract = {Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. However, Electroencephalogram (EEG) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open-eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task. © 2011 IEEE.},
note = {cited By 6},
keywords = {Autism, Brain Function, Brain Signals, Classification Process, Data Dimensions, Diseases, Electroencephalogram Signals, Electroencephalography, Frequency Domain Analysis, Industrial Electronics, Motor Movements, Motor Tasks, PCA, Principal Component Analysis, Signal Detection, Time Frequency Domain},
pubstate = {published},
tppubtype = {conference}
}