DEVAE-GAN:多级认知工作负荷水平fNIRS数据生成模型  

DEVAE-GAN:A Generative Model of the fNIRS Data under Multiple Levels of the Cognitive Workload

作　　者：陈利 马壮 尹钟[1] CHEN Li;MA Zhuang;YIN Zhong(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子科技》2025年第4期31-38,58,共9页Electronic Science and Technology

基　　金：国家自然科学基金(61703277);上海青年科技英才扬帆计划(17YF1427000)。

摘　　要：深度学习方法在fNIRS(functional Near-Infrared Spectroscopy)的应用已成为脑机接口领域的研究热点,但较少的可用数据限制了深度学习模型的性能。文中基于DEVAE-GAN(Dual-Encoder-Variational Autoencoder-Generative Adversarial Network)提出适用于fNIRS原始信号生成的方法。将预处理好的fNIRS信号转换为时间和空间表示形式,输入到双编码器中提取时间和空间信息,拼接两条信息并送入解码器中生成样本。为了验证其有效性,在心理负荷任务的公开数据集上进行实验,将不同数量的生成样本扩充到训练数据集,并使用增强的数据集来训练深度神经网络。与多个基线生成模型的对比表明,所提方法生成的样本质量最高,使用该方法后所有被试的平均分类准确率为95.86%,与原始数据集相比提升了0.91%。实验结果表明,所提方法可以有效学习心理负荷任务fNIRS原始数据的分布,生成高质量的样本,提升深度学习模型的性能。The application of deep learning methods to fNIRS(functional Near-Infrared Spectroscopy)has become a research hotspot in the field of brain computer interface,but less available data limits the performance of deep learning models.A method for generating fNIRS original signal is proposed based on DEVAE-GAN(Dual-Encoder-Variational Autoencoder-Generative Adversarial Network)in this study.In this method,the pre-processed fNIRS signals are converted into time and space representations,input into a dual encoder to extract time and space information,splice two pieces of information,and send to the decoder to generate samples.In order to verify its effectiveness,experiments are conducted on public data sets of mental load tasks,and different numbers of generated samples are extended to the training data set,and the enhanced data set is used to train the deep neural network.Compared with multiple baseline generation models,the proposed method generates the highest sample quality,and the average classification accuracy of all subjects after using this method is 95.86%,which is increased by 0.91%when compared with the original data set.The experimental results show that the proposed method can effectively learn the distribution of raw data of mental load task fNIRS,generate high-quality samples,and improve the performance of deep learning models.

关 键 词：FNIRS DEVAE-GAN 数据生成 心理负荷任务 深度学习 生成模型 脑机接口 深度神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]