AUTHOR=Peng Shixin , Xu Ruyi , Yi Xin , Hu Xin , Liu Lili , Liu Leyuan 

TITLE=Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With L1-Norm Regularization

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2021.656578

DOI=10.3389/fnhum.2021.656578

ISSN=1662-5161

ABSTRACT=Early Screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for the children with Autism Spectrum Disorder (ASD). Research has shown that Electroencephalogram (EEG) signals can reflect abnormal brain function of the children with ASD and screening with EEG signals has the characteristics of good real-time performance and high sensitivity. However, the existing EEG screening algorithms mostly focus on the data analysis in the resting state and the extracted EEG features have some drawbacks such as weak representation capacity and information redundancy. In this work, we utilize Event-related Potential (ERP) technique to acquire testees’ EEG data under positive and negative emotional stimulation and propose a EEG Feature Selection Algorithm based on L1-norm Regularization to carry out screening of autism. The proposed EEG Feature Selection Algorithm include the following steps: 1) Extracting 20 EEG features from raw data; 2) Classification with Support Vector Machine (SVM); 3) Selecting appropriate EEG feature with L1- norm Regularization according to the classification performance. The experimental results shows that the accuracy for screening of the children with ASD can reach 93.8% and 87.5% under positive and negative emotional stimulation and the proposed algorithm can effectively eliminate redundant features and improve screening accuracy.