Microsleeps are brief lapses in consciousness with complete suspension of performance. They are the cause of fatal accidents in many transport sectors requiring sustained attention, especially driving. A microsleep-warning device, using wireless EEG electrodes, could be used to rouse a user from an imminent microsleep. High-dimensional datasets, especially in EEG-based classification, present challenges as there are often a large number of potentially useful features for detecting the phenomenon of interest. Thus, it is often important to reduce the dimension of the original data prior to training the classifier. In this study, linear dimensionality reduction methods—principal component analysis (PCA) and probabilistic PCA (PPCA)—were compared with eight non-linear dimensionality reduction methods (kernel PCA, classical multi-dimensional scaling, isometric mapping, nearest neighbour estimation, stochastic neighbourhood embedding, autoencoder, stochastic proximity embedding, and Laplacian eigenmaps) on previously collected behavioural and EEG data from eight healthy non-sleep-deprived volunteers performing a 1D-visuomotor tracking task for 1 h. The effectiveness of the feature reduction algorithms was evaluated by visual inspection of class separation on 3D scatterplots, by trustworthiness scores, and by microsleep detection performance on a stacked-generalisation-based linear discriminant analysis (LDA) system estimating the microsleep/responsive state at 1 Hz based on the reduced features. On trustworthiness, PPCA outperformed PCA, but PCA outperformed all of the non-linear techniques. The trustworthiness score for each feature reduction method also correlated strongly with microsleep-state detection performance, providing strong validation of the ability of trustworthiness to estimate the relative effectiveness of feature reduction approaches, in terms of predicting performance, and ability to do so independently of the gold standard.

微睡眠是意识的短暂失误，表现完全暂停。它们是许多需要持续关注，尤其是驾驶的交通部门发生致命事故的原因。使用无线 EEG 电极的微睡眠警告设备可用于将用户从即将进入的微睡眠中唤醒。高维数据集，尤其是在基于 EEG 的分类中，存在挑战，因为通常有大量潜在有用的特征可用于检测感兴趣的现象。因此，在训练分类器之前降低原始数据的维度通常很重要。在这项研究中，线性降维方法——主成分分析 (PCA) 和概率 PCA (PPCA)——与八种非线性降维方法（核 PCA、经典多维缩放、等距映射、最近邻估计、随机邻域嵌入、自动编码器、随机邻近嵌入和拉普拉斯特征图）对先前收集的来自八名健康非睡眠剥夺志愿者的行为和 EEG 数据执行 1 小时的一维视觉运动跟踪任务。通过对 3D 散点图上的类别分离进行目视检查、可信度分数以及基于堆叠泛化的线性判别分析 (LDA) 系统上的微睡眠检测性能来评估特征减少算法的有效性，该系统估计微睡眠/响应状态为 1 Hz 基于减少的特征。在可信度方面，PPCA 优于 PCA，但 PCA 优于所有非线性技术。