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Autism is a neurodevelopmental disorder marked by a lack of interpersonal, social, and communication skills, and repetitive and limited behavioral patterns. Autism exhibits a different severity and level of functioning, ranging from high functioning (HF) to low functioning (LF), as per their intellectual/developmental abilities. Identifying the level of the functionality remains crucial in treating and training Autistic children. Brain functioning widely varies during task performance, and the consequent neural representations can act as potential biomarkers. This work aims to classify LF, and HF autistic children based on electroencephalography (EEG), a promising neural underpinning technique acquired while performing cognitive tasks. But task-based EEG data acquisition from autistic children remains challenging and hinders effective classification procedures due to inadequate sample sizes and difficulty acquiring artifact-free signals. Acquired EEG signals were pre-processed for artifact removal to overcome this limitation, and a Gaussian bootstrapping data augmentation technique was employed to strengthen the dataset. Frequency band-specific absolute power, relative power, and derived parameters such as theta-to-alpha ratio and theta-to-beta ratio were extracted. The statistically relevant features were considered for classifying high and low-functioning autism using a kernel-based support vector machine (SVM). Results show that the fine gaussian kernel outperformed the other kernel methods with an average accuracy of 99.9% in the classification process. Thus, this work seems to effectively employ task-based EEG parameters to distinguish the LF and HF groups rather than depending on behavioral measures to categorize autism.
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