IEEE Trans Cybern. 2021 Nov;51(11):5483-5496. doi: 10.1109/TCYB.2020.2977267. Epub 2021 Nov 9.
Pilots' brain fatigue status recognition faces two important issues. They are how to extract brain cognitive features and how to identify these fatigue characteristics. In this article, a gamma deep belief network is proposed to extract multilayer deep representations of high-dimensional cognitive data. The Dirichlet distributed connection weight vector is upsampled layer by layer in each iteration, and then the hidden units of the gamma distribution are downsampled. An effective upper and lower Gibbs sampler is formed to realize the automatic reasoning of the network structure. In order to extract the 3-D instantaneous time-frequency distribution spectrum of electroencephalogram (EEG) signals and avoid signal modal aliasing, this article also proposes a smoothed pseudo affine Wigner-Ville distribution method. Finally, experimental results show that our model achieves satisfactory results in terms of both recognition accuracy and stability.
飞行员脑疲劳状态识别面临两个重要问题。它们是如何提取脑认知特征以及如何识别这些疲劳特征。在本文中，提出了一种伽马深度置信网络来提取高维认知数据的多层深度表示。在每次迭代中，Dirichlet 分布的连接权向量逐层上采样，然后对伽马分布的隐单元进行下采样。形成有效的上下 Gibbs 采样器，实现网络结构的自动推理。为了提取脑电（EEG）信号的 3-D 瞬时时频分布谱并避免信号模态混叠，本文还提出了一种平滑伪仿射 Wigner-Ville 分布方法。最后，实验结果表明，我们的模型在识别精度和稳定性方面都取得了令人满意的结果。
