@conference{HelmiAdly20141125,
title = {Serious game for autism children: Conceptual framework},
author = {M N Helmi Adly and S Faaizah and C P Naim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903171670&doi=10.2495%2fICTE131392&partnerID=40&md5=ce514b70cd03f5ce4a27685992a45b17},
doi = {10.2495/ICTE131392},
issn = {17433517},
year = {2014},
date = {2014-01-01},
journal = {WIT Transactions on Information and Communication Technologies},
volume = {58 VOL I},
pages = {1125-1132},
publisher = {WITPress},
abstract = {An organized conceptual framework is an important component to acquired better understanding of prototype development. At this time, a systematic diagnose has been developed to assess visual perception problem for autism children. However, the method for diagnosing is still conducted manually and hands-on technique. In this paper, we present a conceptual framework to diagnose and assessing visual perception problem for autism children by using serious digital game. It will be used as a reference to construct a prototype using Adobe Flash software. This framework will be a technical solution from intervention to improve visual perception skills among autism children. The outcome from this research framework can be used for educational area and medical field. © 2014 WIT Press.},
note = {cited By 1},
keywords = {Autism, Communication Systems, Computer Games, Conceptual Framework, Diagnosis, Digital Games, Diseases, Education, Information Technology, Prototype Development, Research, Software Prototyping, Technical Solutions, Vision, Visual Perception},
pubstate = {published},
tppubtype = {conference}
}

@conference{Shams2013,
title = {Source-temporal-features for detection EEG behavior of autism spectrum disorder},
author = {W K Shams and A Wahab},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879037124&doi=10.1109%2fICT4M.2013.6518913&partnerID=40&md5=db31715811e1e8fdf62c9d61daf8e6f6},
doi = {10.1109/ICT4M.2013.6518913},
isbn = {9781479901340},
year = {2013},
date = {2013-01-01},
journal = {2013 5th International Conference on Information and Communication Technology for the Muslim World, ICT4M 2013},
abstract = {This study introduces a new model to capture the abnormal brain activity of children with Autism Spectrum Disorder (ASD) during eyes open and eyes closed resting conditions. EEG data was collected from normal subjects' ages (4 to 9) years and ASD subjects match group. Time Difference of Arrival (TDOA) approach was applied with EEG data raw for feature extracted at time domain. The neural network, Multilayer Perception (MLP) was used to distinguish between the two groups during the two tasks. Results show significant accuracy around 98% for both tasks and clearly discriminate for the features in z-dimension his electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. © 2013 IEEE.},
note = {cited By 1},
keywords = {ASD, Autism Spectrum Disorders, Brain Activity, Children with Autism, Classification (of information), Communication, Diseases, Electroencephalography, Electronic Document, Information Technology, Multi-Layer Perception, Temporal Features, Time Difference of Arrival},
pubstate = {published},
tppubtype = {conference}
}

@conference{Othman2010,
title = {Affective face processing analysis in autism using electroencephalogram},
author = {M Othman and A Wahab},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052372671&doi=10.1109%2fICT4M.2010.5971907&partnerID=40&md5=4d5f8a317d6a9c93e1ab7186a9b99b52},
doi = {10.1109/ICT4M.2010.5971907},
isbn = {9789791948913},
year = {2010},
date = {2010-01-01},
journal = {Proceeding of the 3rd International Conference on Information and Communication Technology for the Moslem World: ICT Connecting Cultures, ICT4M 2010},
pages = {E23-E27},
abstract = {Past research in the area of psychology has indicated the inability of Autism Spectrum Disorder (ASD) patients for interpreting other people's emotion. This impairment is due to their lack of social motivation and eye contact during communication, causing insufficient information to the brain for interpreting emotional faces. This paper investigates human brainwaves for understanding affective face processing of ASD children. Pattern classification results are explained based on the 2-dimensional emotion model. The 2-dimensional model explains human emotion in terms of the pleasant/ unpleasantness (or valence) and intensity (or arousal). Analysis results revealed that emotion of the non-autistic group is altered towards matching the affective faces currently displayed on the computer monitor. Emotion dynamics of ASD children, however, indicated the trend for reversed valence while watching emotionally related facial expressions. © 2010 IEEE.},
note = {cited By 7},
keywords = {Affective Face Processing, Analysis Results, Autism Spectrum Disorders, Brain Wave, Diseases, Electroencephalogram, Electroencephalography, Emotion, Emotion Models, Eye Contact, Facial Expression, Human Emotion, Information Technology},
pubstate = {published},
tppubtype = {conference}
}

@conference{Razali2010,
title = {Motor movement for autism spectrum disorder (ASD) detection},
author = {N Razali and A W A Rahman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052346152&doi=10.1109%2fICT4M.2010.5971921&partnerID=40&md5=234cdd8f3906ad980ed163a1036215ee},
doi = {10.1109/ICT4M.2010.5971921},
isbn = {9789791948913},
year = {2010},
date = {2010-01-01},
journal = {Proceeding of the 3rd International Conference on Information and Communication Technology for the Moslem World: ICT Connecting Cultures, ICT4M 2010},
pages = {E90-E95},
abstract = {In this paper, we are looking at the differences between autistic and normal children in term of fine motor movement. Previous findings have shown that there are differences between autistic children and normal children when performing a simple motor movement tasks. Imitating a finger tapping and clinching a hand are two examples of a simple motor movement tasks. Our study had adopted one of the video stimuli for clinching the hand from Brainmarkers. 6 selected autistic children and 6 selected normal children were involved in this study. The data collection is using EEG device and will be analyzed using Gaussian mixture model (GMM) and Multilayer perceptron (MLP) as classifier to discriminate between autistic and normal children. Experimental result shows the potential of verifying between autistic and normal children with accuracy of 92%. The potential of using these techniques to identify autistic children can help early detection for the purpose of early intervention. Moreover, the spectrums of the signals also present big differences between the two groups. © 2010 IEEE.},
note = {cited By 3},
keywords = {Autism, Autism Spectrum Disorders, Autistic Children, Children with Autism, Data Collection, Diseases, Early Detection, Early Intervention, Finger Tapping, Gaussian Mixture Model, Information Technology, Motor Movements, Multi Layer Perceptron, Multilayer Perceptron (MLP), Multilayers},
pubstate = {published},
tppubtype = {conference}
}