Autism spectrum disorders (ASD) is a complex developmental disorder characterized by impairments in social communication and stereotyped behaviors. Electroencephalograph (EEG), which can measure neurological changes associated with cortical synaptic activity, has been proven to be a powerful tool for detecting neurological disorders. The main goal of this study is to explore the effects of repetitive transcranial magnetic stimulation (rTMS) on behavioral response and EEG. We enrolled 32 autistic children, rTMS group ( = 16) and control group ( = 16) and calculated the relative power of the δ, θ, α, β rhythms in each brain area by fast Fourier transform and Welch's method. We also compared Autism Behavior Checklist (ABC) scores of the patients before and after rTMS. The results showed a significant decrease in the relative power of the δ band on right temporal region and parietal region and also a decreased coherence on frontal region after rTMS intervention. The study proves that rTMS could have positive effects on behavior of attention, execution ability, and language ability of children and could reduce their stereotyped behavior and radical behavior.

Early detection and timely intervention are very essential for autism. This paper used the wavelet transform and empirical mode decomposition (EMD) to extract the features of electroencephalogram (EEG), to compare the feature differences of EEG between the autistic children and healthy children. The experimental subjects included 25 healthy children (aged 5-10 years old) and 25 children with autism (20 boys and 5 girls aged 5-10 years old) respectively. The alpha, beta, theta and delta rhythm wave spectra of the C3, C4, F3, F4, F7, F8, FP1, FP2, O1, O2, P3, P4, T3, T4, T5 and T6 channels were extracted and decomposed by EMD decomposition to obtain the intrinsic modal functions. Finally the support vector machine (SVM) classifier was used to implement assessment of autism and normal classification. The results showed that the accuracy could reach 87% and which was nearly 20% higher than that of the model combining the wavelet transform and sample entropy in the paper. Moreover, the accuracy of delta (1-4 Hz) rhythm wave was the highest among the four kinds of rhythms. And the classification accuracy of the forehead F7 channel, left FP1 channel and T6 channel in the temporal region were all up to 90%, which expressed the characteristics of EEG signals in autistic children better.