Early Detection On Autistic Children by Using EEG Signals
Keywords:
Electroencephalography (EEG), Autism, Autistic kids/ childrenAbstract
The number of autistic children has been increased in Malaysia throughout the year. The assessment that available for autism diagnosis is very limited since it involves expert to diagnose the disease. The assessment of autism by using neurophysiological signals has been found as scarce particularly in Malaysia. Thus, this research study has been engineered using EEG signals to early detect if the subjects are having autism to use affective computing to do the identification of autistic children. The brain signal was collected from the subjects aged from 4 to 5 years using a 19 channel EEG machine called the DABO machine. Objective of this research is to focus on early detection if the subjects are having autism and using effective computing to do the identification. In addition, the aim also demands to note the difference in emotion levels between the subject and the normal group. As far as the methodology of this research is concerned, we center around five distinct states to finish the experiment. These states are the collection of EEG data (raw Data), data pre-processing (filter noise), features extraction which will be analysed using Mel Frequency Cepstral Coefficients or MFCC, classification which will be classified using multilayer perceptron or MLP and lastly the final result. Result shows that there is significant different emotion appear between normal subject and subject with autism. This will benefit the caregiver or parents and also researcher to identify the condition of the children through this early detection
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