1. 常州工业职业技术学院信息工程学院
2. 北京理工大学物理学院
3. 江苏理工学院计算机工程学院
纸质出版:2025
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袁凯烽, 侯璐, 黄永锋. 基于半监督VAE和CGAN的运动想象脑电信号分类器[J]. 传感器与微系统, 2025,(2):82-86.
袁凯烽, 侯璐, 黄永锋. 基于半监督VAE和CGAN的运动想象脑电信号分类器[J]. 传感器与微系统, 2025,(2):82-86. DOI: 10.13873/J.1000-9787(2025)02-0082-05.
由于脑电(EEG)信号的特异性、隐私性,数据集相对匮乏,运动想象EEG(MI-EEG)信号分类是一项具有挑战性的任务。提出了一种基于半监督变分自编码器—条件生成对抗网络(SSVAE-CGAN)模型应用于MI-EEG信号的增强和分类。SSVAE-CGAN模型的SSVAE-CGAN的编码器为EEGNet网络,获得MI-EEG信号的时域、频域和空间域的复合特征的潜在空间表示。不同于传统的无监督变分自编码器,在训练编码器时,SSVAE-CGAN使用MI-EEG信号的标签信息以监督的方式更好地构建潜在空间。然后,SSVAE-CGAN使用条件生成对抗网络接收带有标签信息的随机噪声进行生成器—判别器的对抗训练,并生成与潜在空间分布对齐的隐空间。在真实MI-EEG数据集进行了数据增强和分类实验,实验结果验证了本文模型的有效性。
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ichael eTangermann,Klaus-Robert eMüller,Ad eAertsen,Ad eAertsen,Niels eBirbaumer,Niels eBirbaumer,Christoph eBraun,Christoph eBraun,Clemens eBrunner,Clemens eBrunner,Robert eLeeb,Carsten eMehring,Carsten eMehring,Carsten eMehring,Kai J Miller,Gernot eMueller-Putz,Guido eNolte,Gert ePfurtscheller,Hubert ePreissl,Hubert ePreissl,Gerwin eSchalk,Gerwin eSchalk,Gerwin eSchalk,Gerwin eSchalk,Gerwin eSchalk,Alois eSchlögl,Carmen eVidaurre,Stephan eWaldert,Stephan eWaldert,Stephan eWald.Review of the BCI Competition IV[J].Frontiers in Neuroscience,2012.
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