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In neuropsychological disorders, the significant abnormalities in the brain connections in some regions are observed. This paper presents a novel model to demonstrate the connections between different regions in children with autism. The proposed model first conducts the wavelet decomposition of electroencephalography signals by wavelet transform then the features are extracted, such as relative energy and entropy. These features are fed to the 3D-cellular neural network model as inputs to indicate the brain connections. The results showed that there are significant differences and abnormalities in the left hemisphere, (p<0.05) at the electrodes AF3, F3, P7, T7 and O1 in alpha band, AF3, F7, T7 and O1 in beta band, T7 and P7 in gamma band for children with autism compared with the control children. Also, the evaluation of the obtained connections values between brain regions indicated that there are more abnormalities in the connectivity of frontal and parietal lobes and the relations of the neighboring regions in all three bands especially in gamma band for autistic children. Evaluation of the analysis demonstrated that alpha frequency band had the best distinction level of 96.6% based on the obtained values of the cellular neural network using support vector machine method.

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In neuropsychological disorders significant abnormalities in brain connectivity are observed in some regions. A novel model demonstrates connectivity between different brain regions in children with autism. Wavelet decomposition is used to extract features such as relative energy and entropy from electroencephalograph signals. These features are used as input to a 3Dcellular neural network model that indicates brain connectivity. Results show significant differences and abnormalities in the left hemisphere, (p < 0.05) at electrodes AF3, F3, P7, T7, and O1 in the alpha band, AF3, F7, T7, and O1 in the beta band, and T7 and P7 in the gamma band for children with autism when compared with non-autistic controls. Abnormalities in the connectivity of frontal and parietal lobes and the relations of neighboring regions for all three bands (particularly the gamma band) were detected for autistic children. Evaluation demonstrated the alpha frequency band had the best level of distinction (96.6%) based on the values obtained from a cellular neural network that employed support vector machine methods.Many investigators have studied the EEGs and analyzed the signals of autistic subjects [14][15][16]. Sheikhani et al.[15] conducted a study of EEG signals based on Lempel-Ziv frequency methods (LZ) and the short-time Fourier transform (STFT). After evaluating results,