Journal
FRONTIERS IN HUMAN NEUROSCIENCE
Volume 15, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnhum.2021.656578
Keywords
early screening; autism spectrum disorder; electroencephalogram signal; feature selection; event-related potential
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Funding
- Ministry of Education (MOE) in China Project of Humanities and Social Sciences [19YJC880068]
- Hubei Provincial Natural Science Foundation of China [2019CFB347]
- China Postdoctoral Science Foundation [2018M632889]
- Research project of graduate teaching reform of CCNU [2019JG01]
- National Natural Science Foundation of China [61702208]
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Early screening is crucial for children with autism spectrum disorder (ASD) to receive intensive intervention and rehabilitation therapy. EEG signals can reflect abnormal brain function of children with ASD, and a proposed EEG Feature Selection Algorithm based on L1-norm regularization can effectively screen for autism with high accuracy and eliminate redundant features.
Early screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for children with autism spectrum disorder (ASD). Research has shown that electroencephalogram (EEG) signals can reflect abnormal brain function of 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 disadvantages such as weak representation capacity and information redundancy. In this study, we utilized the event-related potential (ERP) technique to acquire the EEG data of the subjects under positive and negative emotional stimulation and proposed an EEG Feature Selection Algorithm based on L1-norm regularization to perform screening of autism. The proposed EEG Feature Selection Algorithm includes the following steps: (1) extracting 20 EEG features from the raw data, (2) classification with support vector machine, (3) selecting appropriate EEG feature with L1-norm regularization according to the classification performance. The experimental results show that the accuracy for screening of 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.
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