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Facial Emotion Processing in Autism Spectrum Disorder Based on Spectral Features of EEG Signals

Mina Hashemian, Hossein Pourghassem


Face is the most potent visual stimulus in human communication. The recognition of facial emotions is an index of development of individual social and cognitive skills. Since, one of the characteristics of people with autism is inability in making interpersonal interactions. The recognition issue of facial emotions has been widely focused on in recent years. Some results imply that autistics demonstrate different performances in recognizing facial emotions compared with normal people. In this paper, a novel facial emotion processing algorithm based on spectral features in EEG signals of autism spectrum disorder is proposed. For this purpose, the EEG signals of autistic and normal individuals are processed in the sad faces looking state. Then, the spectral features of signals are extracted and genetic algorithm is applied to select the best features in the feature vector. Three classifiers, K-nearest Neighborhood (KNN), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) obtained accuracy rate of 98.13%, 96.47% and 95.93%, respectively.


Autism, facial emotion processing, sad faces, EEG signals.

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