This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots’ cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.

中文翻译：

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通过大脑认知图谱和多层潜在增量学习模型进行飞行员大脑疲劳检测


这项工作提出了一种非参数先验诱导深度和对数多项混合（DSLMM）模型，通过开发的脑力图来检测飞行员的认知状态。 DSLMM 使用多重正态分布来推断网络第一层中每个神经元的潜在变量。这些潜在变量服从和对数分布，该分布反向传播到其观察向量和下一层中的神经元数量。使用多重正态分布对扩展的观测向量进行分段，形成与下一层的宽度相关的矩阵。这项工作还提出了一种自适应主题层随机梯度黎曼（ATL-SGR）马尔可夫链蒙特卡罗（MCMC）推理方法来学习其全局参数而无需启发式假设。实验结果表明，DSLMM能够逐层提取脑力图中包含的更多概率分布，实现更高的飞行员认知检测精度。