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Published on: July 31, 2017
Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With
Shixin Peng1,2, Ruyi Xu1,2, Xin Yi1,2
1National Engineering Laboratory for Education Big Data, Faculty of Artificial Intelligence Education, Central China Normal University, Wuhan, China.
Insights
Early screening for autism spectrum disorder (ASD) is improved using electroencephalogram (EEG) event-related potentials. A new L1-norm regularization algorithm enhances EEG feature selection, boosting screening accuracy.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Developmental Psychology
Background:
- Early screening for autism spectrum disorder (ASD) is crucial for timely intervention.
- Electroencephalogram (EEG) signals show potential for ASD screening due to real-time performance and high sensitivity.
- Existing EEG screening methods often rely on resting-state data, leading to limited feature representation and redundancy.
Purpose of the Study:
- To propose a novel EEG feature selection algorithm for improved ASD screening.
- To utilize event-related potential (ERP) technique under emotional stimulation for enhanced EEG data acquisition.
- To address limitations of existing algorithms by reducing feature redundancy and improving classification accuracy.
Main Methods:
- Acquired EEG data using event-related potential (ERP) technique under positive and negative emotional stimuli.
- Extracted 20 EEG features from raw data.
- Employed an EEG Feature Selection Algorithm based on L1-norm regularization for classification using a support vector machine (SVM).
Main Results:
- Achieved high screening accuracy for ASD: 93.8% under positive emotional stimulation and 87.5% under negative emotional stimulation.
- Demonstrated the algorithm's effectiveness in eliminating redundant EEG features.
- Showcased significant improvement in overall screening accuracy through optimized feature selection.
Conclusions:
- The proposed L1-norm regularization-based EEG feature selection algorithm effectively screens children with ASD.
- Utilizing ERPs under emotional stimulation provides a sensitive method for capturing brain function abnormalities in ASD.
- This approach offers a promising, accurate, and efficient tool for early ASD detection.
Abstract:
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.
