فصلنامه روانشناسی شناختی

Anxiety is a natural reaction of humans to stress that occurs in the face of various factors. Anxiety is considered as a mental illness if it is excessive and uncontrollable in the form of fear and anxiety. Today, clinicians use certain criteria to diagnose anxiety disorders. This analytical-observational study was aimed at automatically classifying the two levels of anxious and normal by analyzing electroencephalogram signals. In this paper, the DASPS database was used, which contains a 14-channel electroencephalogram of 23 people (13 females and 10 males, mean age 30 years) during anxiety. Anxiety was presented in the form of flooding as actual exposure to the feared stimulus. Based on the results of the Self-Assessment Manikin, data were divided into two groups: (1) normal and low anxiety and (2) moderate and high anxiety. Approximate entropy, fractal dimension, and Lyapunov exponents were extracted from all channels as nonlinear properties. Maximum relevance and minimum redundancy were used to select the best feature to apply to the multilayer perceptron network. To evaluate the performance of the algorithm, different network structures were examined in terms of the number of features and neurons as well as different feature dimensions. Maximum accuracy, precision, f1-score, and sensitivity in 20 repetitions in all cases is equal to 100, and with an increasing number of neurons, the average accuracy increases. The best results were obtained for 5 features and 15 neurons, where the mean accuracy, precision, f1-score, and sensitivity for it were 80%, 92.75%, 84.15%, and 80.58%, respectively. The results of this paper indicated the capability of the proposed algorithm to distinguish anxious people from normal ones.

Recent neuroimaging studies have shown that main symptoms of Autism Spectrum Disorder (ASD) such as deficits in social communication, speech and repetitive behaviors are associated with abnormalities in neural connectivity. The abnormalities in neural connectivity have been studied by several methods. Among these methods, electroencephalography is an efficient and a non-invasive tool that records brain electrical activity and helps us to gain information about brain neural connectivity and cognitive characters. Therefore, the purpose of this study was to analyze electroencephalogram resting state data to compare brain connectivity (coherence) patterns between children with ASD and typically developing children. The method of this study was descriptive-analytical. The population of the study consisted of all children with ASD (aged 6-13) referred to psychologists in Mehraz Andisheh Clinic in Shiraz. Fifteen children with ASD (boys = 11 and girls = 4) were selected via purposeful sampling method. Moreover, a group of fifteen typically developing children who were matched based on chronological age and gender were recruited. Quantitative Electroencephalography data analyses showed a significant difference between the two groups and indicating hyper connectivity in most frequency bands among children with ASD. Therefore, quantitative electroencephalography patterns of children with ASD indicated an increase in the levels of coherence in delta (p < .05) and theta (p < .05) powers in the prefrontal region, theta (p < .05) and alpha (p < .05) waves in the central region, in theta (p < .001), alpha (p < .001) and beta (p < .001) waves in the occipital region, in addition to delta (p < .001), theta (p < .001) and alpha (p < .001) waves in the temporal region. The findings demonstrated abnormalities in brain connectivity (coherence) patterns of children with ASD which is supported by cortical connectivity theory. Consequently, these findings (hyper connectivity patterns) can be considered as a useful marker to better diagnose ASD. Moreover, changing these patterns may have a positive impact on the treatment of individuals with ASD.