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

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 validatio

Dynamic analysis of critical features in EEG for motor imitation among Autistic children

Research study among children with autism had shown impairment in motor imitation in addition to social disability. Currently, imitation becomes an important issue which can be seen as new procedure to detect early childhood autism. Hence, this paper proposed the used of motor imitation action by analyzing the brain waves frequency. Experimental results revealed that control and autistic children both perform the motor imitation well but the brain activation for both group are different. Autistic children demonstrate a very high intensity brain activation indicating they are struggling to do the action. This illustrates some potential methods that can be extended in detecting autism for early childhood