@conference{Hoole2012541,
title = {Autism, EEG and brain electromagnetics research},
author = {P R P Hoole and K Pirapaharan and S A Basar and R Ismail and D L D A Liyanage and S S H M U L Senanayake and S R H Hoole},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876771339&doi=10.1109%2fIECBES.2012.6498036&partnerID=40&md5=9f9390b30b859a90936c66699c1a5115},
doi = {10.1109/IECBES.2012.6498036},
isbn = {9781467316668},
year = {2012},
date = {2012-01-01},
journal = {2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012},
pages = {541-543},
abstract = {There has been a significant increase in the incidence of autism. We report the work on autism by our international group, on the growing attention paid to EEG based diagnosis and the interest in tracing EEG changes to brain electromagnetic signals (BEMS), seeking the cause of autism and the brain regions of its origin. The time- and frequency domain and principal component analysis (PCA) of these EEG signals with a Multilayer Perception Neural Network (MLP) identifies an autistic subject and helps improve classification accuracy. We show differences between a working brain and a relaxed brain, especially in the Alpha waves used for diagnosis. © 2012 IEEE.},
note = {cited By 11},
keywords = {Biomedical Engineering, Brain, Brain Regions, Classification Accuracy, Diseases, EEG Signals, Electromagnetic Signals, Electromagnetics, Electromagnetism, Frequency Domains, International Group, Multilayer Perception Neural Networks, Neuroimaging, Principal Component Analysis},
pubstate = {published},
tppubtype = {conference}
}

@conference{Yee20126,
title = {Mobile technology for children with autism spectrum disorder: Major trends and issues},
author = {H S S Yee},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874045323&doi=10.1109%2fIS3e.2012.6414954&partnerID=40&md5=7ce6fc2bfa0651860ccbc3b48c67e1eb},
doi = {10.1109/IS3e.2012.6414954},
isbn = {9781467323895},
year = {2012},
date = {2012-01-01},
journal = {2012 IEEE Symposium on E-Learning, E-Management and E-Services, IS3e 2012},
pages = {6-10},
abstract = {Mobile devices had gained popularity among the special needs community. These mobile devices are the new and cool gadgets to be seen with, unlike the óld', complex and 'I-am-not-normal-looking' assistive devices. These mobile devices were said to serve as a communication device in the pocket, a learning device on the go and even a lifesaver for some. Among the features are its flexible multimedia content and storage, portability, mobility and affordability. The touch screen interface makes it appealing and simple to use, particularly for those who have weak fine motor skills. It offers practical communication solutions for autistic persons in relating to their families and others in the community. The flexibility and the advanced capabilities of mobile technology are opening new opportunities for further research in the area of computer-based intervention for children with ASD. Several anecdotal reports gave an early indication of the immense possibilities of how these devices could play a significant role in enhancing the quality of life of the children with ASD and their families. There is definitely lack of published research studies on the use of mobile technology with children with ASD. Due to the growing popularity of adopting mobile devices as assistive devices, more in depth research in warranted. © 2012 IEEE.},
note = {cited By 17},
keywords = {Assistive Technology, Autism, Computer Technology, Diseases, E-learning, Mobile Devices, Mobile Technology, Mobile Telecommunication Systems, Research, Trends},
pubstate = {published},
tppubtype = {conference}
}

@conference{Shamsuddin2012188,
title = {Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO},
author = {S Shamsuddin and H Yussof and L Ismail and F A Hanapiah and S Mohamed and H A Piah and N I Zahari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861537641&doi=10.1109%2fCSPA.2012.6194716&partnerID=40&md5=32572eb3ebc7d201c02a90908128ae28},
doi = {10.1109/CSPA.2012.6194716},
isbn = {9781467309615},
year = {2012},
date = {2012-01-01},
journal = {Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012},
pages = {188-193},
abstract = {The overall context proposed in this paper is part of our long-standing goal to contribute to a group of community that suffers from Autism Spectrum Disorder (ASD); a lifelong developmental disability. The objective of this paper is to present the development of our pilot experiment protocol where children with ASD will be exposed to the humanoid robot NAO. This fully programmable humanoid offers an ideal research platform for human-robot interaction (HRI). This study serves as the platform for fundamental investigation to observe the initial response and behavior of the children in the said environment. The system utilizes external cameras, besides the robot's own visual system. Anticipated results are the real initial response and reaction of ASD children during the HRI with the humanoid robot. This shall leads to adaptation of new procedures in ASD therapy based on HRI, especially for a non-technical-expert person to be involved in the robotics intervention during the therapy session. © 2012 IEEE.},
note = {cited By 103},
keywords = {Anthropomorphic Robots, Autism Spectrum Disorders, Autistic Children, Children with Autism, Developmental Disorders, Diseases, Human Computer Interaction, Human Robot Interaction, Humanoid Robot, Man Machine Systems, Pilot Experiment, Rehabilitation Robotics, Research, Robotics, Signal Processing, Visual Systems},
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}
}

@conference{Ismail2011517,
title = {Face detection technique of Humanoid Robot NAO for application in robotic assistive therapy},
author = {L Ismail and S Shamsuddin and H Yussof and H Hashim and S Bahari and A Jaafar and I Zahari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862067305&doi=10.1109%2fICCSCE.2011.6190580&partnerID=40&md5=954caf63c5c5f7f05062436598a32a91},
doi = {10.1109/ICCSCE.2011.6190580},
isbn = {9781457716423},
year = {2011},
date = {2011-01-01},
journal = {Proceedings - 2011 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2011},
pages = {517-521},
abstract = {This paper proposed a face detection method for tracking the faces of children with Autism Spectrum Disorder in a robotic assistive therapy. The face detection is a novel approach in robotic assistive therapy involving autistic children since it is believe that those children will positively react with high-end devices, gadget and cutting edge devices. The intention of tracking the autistic children's faces is to measure the concentration level of the children in social interaction and communication since everyone knows that those children are suffering from communication disabilities and deficits due to brain developmental disorder. Humanoid Robot Nao with 573.2mm height equipped with 2 internal cameras is utilized for this research. The face detection tools in choregraphe and telepathe based on Graphical User Interface (GUI) module is used in this study. The non-verbal interaction between humanoid robot and autistic children is recorded by using 2 internal cameras from the robot's head. The interaction is going to take about 30 minutes and supervised by occupational therapist and certified psychologist. The autistic children will be introduced to the Humanoid Robot Nao and their reaction will be recorded simultaneously while the robot is trying to track their faces. © 2011 IEEE.},
note = {cited By 14},
keywords = {Anthropomorphic Robots, Assistive, Autism Spectrum Disorders, Autistic Children, Cameras, Children with Autism, Communication, Concentration Levels, Control Systems, Cutting Edges, Detection Tools, Developmental Disorders, Diseases, Face Detection, Face Recognition, Graphical User Interfaces, Humanoid Robot, Robotics, Social Interactions},
pubstate = {published},
tppubtype = {conference}
}

@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{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}
}