集成技术2025，Vol.14Issue(3)：78-86,9.DOI:10.12146/j.issn.2095-3135.20241127001
基于条件扩散模型的脑电增强在自闭症筛查中的应用
Application of Electroencephalography Enhancement Based on Conditional Diffusion Model in Autism Screening
摘要
Abstract
With the rapid development of deep learning technology,autism screening based on neural signals such as electroencephalography(EEG)is gradually emerging as a novel diagnostic approach.However,due to the complexity of EEG data acquisition,especially for children,insufficient data often poses a challenge.Data augmentation methods are commonly used to address the scarcity of real-world data,with generative adversarial networks(GANs)being a frequently applied technique.However,due to the limited scale and diversity of data,current augmentation methods have not yet to achieve optimal classification performance.This study introduces an improved conditional diffusion model to enhance both raw EEG signals and their corresponding functional connectivity temporal graphs.Experimental results demonstrate that this method significantly improves autism classification performance,achieving maximum classification accuracies of 84.38％and 79.01％for resting-state and task-state data,respectively.These findings validate the effectiveness of data augmentation based on the conditional diffusion model in enhancing autism screening outcomes.关键词
脑电/脑功能连接时序图/条件扩散模型/数据增强Key words
electroencephalography/time-series maps of brain functional connectivity/conditional diffusion model/data augmentation分类
信息技术与安全科学引用本文复制引用
李逸升,徐永杰,王书强,王怡珊..基于条件扩散模型的脑电增强在自闭症筛查中的应用[J].集成技术,2025,14(3):78-86,9.基金项目
深圳市高层次人才团队项目(KQTD20200820113106007) This work is supported by Shenzhen Science and Technology Program,China(KQTD20200820113106007) （KQTD20200820113106007）
