发表机构
Naqi Logix; Inria Center at Univ. Bordeaux / LaBRI; LITIS, INSA Rouen-Normandy（纳奇逻辑公司; 波尔多大学/LaBRI研究所的Inria中心; 鲁昂-诺曼底高等电子与电工技术工程师学校的LITIS实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究生成模型能否提供合成MI-EEG试验提高下游分类器准确性，训练带集成潜在分类器的CVAE，在两种评估协议下通过四个分类管道测试，发现合成EEG可提高MDM点估计，增益小且依赖分类器。
AI 中文摘要
我们研究生成模型能否提供有用的合成运动想象（MI）脑电图（EEG）试验，以提高独立下游分类器的准确性。我们在周运动想象数据集上训练了一个带有集成潜在分类器的类条件变分自编码器（CVAE），将每个类别的先验知识用作生成器：为给定标签采样先验并将其解码为合成的、标签一致的信号。对生成数据的协方差矩阵的约束鼓励保留协方差结构，并且该模型通过交替进行普通VAE训练和解码器聚焦阶段的训练计划进行训练，该阶段会锐化用于增强的生成路径。我们在两种评估协议下测量向训练集添加合成试验的效果——用户内（跨受试者的60/20/20合并分割）和跨用户（留一受试者法，LOSO）——跨越四个代表性的EEG分类管道：带线性判别分析的共同空间模式（CSP+LDA）、带支持向量机的切线空间特征（TGSP+SVM）、到黎曼均值的最小距离（MDM）以及基于EEGNetv4的神经网络（以下简称EEGNet）。结果通过独立的增强抽取、随机种子（用户内）或留一受试者法折叠（跨用户）进行汇总，不确定性报告为基于每个种子/每个折叠平均值计算的95%置信区间（学生t分布）。我们发现，来自CVAE的合成EEG作为类结构、协方差类数据的来源比作为真实原始EEG的替代品更可信：它可以提高MDM的点估计，但更广泛的增强主张仍然保守——观察到的增益很小且依赖于分类器。
英文摘要
We investigate whether a generative model can supply useful synthetic motor-imagery (MI) electroencephalography (EEG) trials that improve the accuracy of independent downstream classifiers. We train a class-conditional variational autoencoder (CVAE) with an integrated latent classifier on the Zhou motor-imagery dataset, using the learned per-class prior as a generator: sampling the prior for a given label and decoding it into a synthetic, label-consistent signal. A constraint on the covariance matrix of the generated data encourages preservation of covariance structure, and the model is trained with a schedule that alternates ordinary VAE training with a decoder-focused phase that sharpens the generative pathway used for augmentation. We measure the effect of adding synthetic trials to the training set under two evaluation protocols -- within-user (pooled 60/20/20 split across subjects) and cross-user (leave-one-subject-out, LOSO) -- across four representative EEG classification pipelines: Common Spatial Patterns with Linear Discriminant Analysis (CSP+LDA), tangent-space features with a Support Vector Machine (TGSP+SVM), Minimum Distance to Riemannian Mean (MDM), and a neural network based on EEGNetv4 (henceforth EEGNet). Results are aggregated across independent augmentation draws, random seeds (within-user), or leave-one-subject-out folds (cross-user), with uncertainty reported as 95\% confidence intervals (Student's $t$-distribution) computed over per-seed/per-fold averages. We find that synthetic EEG from the CVAE is most credible as a source of class-structured, covariance-like data rather than as a substitute for real raw EEG: it can raise the point estimate for MDM, but the broader augmentation claim remains conservative -- observed gains are small and classifier-dependent.
