Identification of Features to Analyze the Autism Spectrum Disorder Using Electroencephalography
Autism spectrum disorder (ASD) is a heterogeneous neuro developmental disorder which affects the developmental trajectory in several behavioural domains, including impairments of social communication, cognitive and language abilities. In this paper, multi-feature fusion method based on EEG signal is used to extract as many as possible features including power spectrum analysis, bicoherence, entropy and coherence methods, then we use minimum redundancy maximum correlation (mRMR) algorithm to choose the features, which are applied to input to three classifiers to obtain accuracy classification results. We try to find some key biomarkers of ASD by examining the accuracy of classifier, using different models which use the combination of multiplex features. The results show when nine features are selected by SVM-linear classifier, the accuracy is up to 91.38%. This method might provide objective basis for clinical diagnosis of autism. Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuro scientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.