The safety of human–machine systems can be indirectly evaluated based on operator’s cognitive load levels at each temporal instant. However, relevant features of cognitive states are hidden behind in multiple sources of cortical neural responses. In this study, we developed a novel neural network ensemble, SE-SDAE, based on stacked denoising autoencoders (SDAEs) which identify different levels of cognitive load by electroencephalography (EEG) signals. To improve the generalization capability of the ensemble framework, a stacking-based approach is adopted to fuse the abstracted EEG features from activations of deep-structured hidden layers. In particular, we also combine multiple K-nearest neighbor and naive Bayesian classifiers with SDAEs to generate a heterogeneous classification committee to enhance ensemble’s diversity. Finally, we validate the proposed SE-SDAE by comparing its performance with mainstream pattern classifiers for cognitive load evaluation to show its effectiveness.

人机系统的安全性可以根据操作员在每个时间瞬间的认知负荷水平进行间接评估。然而，认知状态的相关特征隐藏在皮层神经反应的多个来源中。在这项研究中，我们开发了一种新的神经网络集合 SE-SDAE，它基于堆叠去噪自动编码器 (SDAE)，它通过脑电图 (EEG) 信号识别不同水平的认知负荷。为了提高集成框架的泛化能力，采用基于堆叠的方法来融合来自深层结构隐藏层激活的抽象 EEG 特征。特别是，我们还将多个 K 最近邻和朴素贝叶斯分类器与 SDAE 相结合，以生成异构分类委员会，以增强集成的多样性。最后，