郑州大学学报（理学版）2026，Vol.58Issue(3)：17-24,8.DOI:10.13705/j.issn.1671-6841.2024152
基于贝叶斯优化WGAN-GP的fNIRS数据增强与情绪识别
fNIRS Data Enhancement and Emotion Recognition Based on Bayesian Optimization WGAN-GP
摘要
Abstract
Collecting large amounts of functional near-infrared spectroscopy(fNIRS)emotion data is a lengthy and tedious process.Limited data can affect training and accuracy of deep learning classification models.To address is issue of a method using Bayesian optimization with gradient penalty for Wasserstein generative adversarial networks(BO-WGAN-GP)was proposed for data augmentation.Extensive emotion classification experiments were conducted on data mixed from original and generated data using different classification models,and comparisons were made with other generative adversarial networks.The experi-mental results showed that data generated by the BO-WGAN-GP model performed best in fNIRS emotion recognition.The average classification accuracies for oxyhemoglobin(HbO2)and deoxyhemoglobin(HbR)reached 97.92%and 99.31%respectively.关键词
生成对抗网络/数据增强/贝叶斯优化/功能性近红外光谱技术/情绪识别Key words
generative adversarial network/data augmentation/Bayesian optimization/functional near-infrared spectroscopy technology/emotion recognition分类
信息技术与安全科学引用本文复制引用
李修军,葛雄心,杨菁菁..基于贝叶斯优化WGAN-GP的fNIRS数据增强与情绪识别[J].郑州大学学报（理学版）,2026,58(3):17-24,8.基金项目
吉林省教育厅科学技术研究项目(JJKH20220780KJ,JJKH20230847KJ) （JJKH20220780KJ,JJKH20230847KJ）
