基于深度主题模型的飞行员脑疲劳检测

Pilot’s Brain Fatigue Detection Based on Deep Topic Model

飞行员脑疲劳状态检测需要解决脑认知图谱生成和脑疲劳检测模型构建问题.针对第一个问题,本文通过等距方位投影法将全脑电极位置的脑疲劳指标映射为二维脑功率图谱,形成一种新型脑认知图谱.针对第二个问题,本文建立一种深度主题学习模型,即深度潜狄利克雷模型(Deep Latent Dirichlet Model,DLDM),解决了飞行员疲劳状态主题学习问题.DLDM深度模型通过多项式分布逐层扩展脑功率图谱中蕴含的概率分布信息,推理脑功率图谱的层次概率分布特征,实现更有效的飞行员疲劳状态主题学习.同时为了避免启发式假设,本文提出一种有效的不同层与主题间自适应学习率的随机梯度下降推断方法,更加高效地推理DLDM网络结构参数.实验结果显示,DLDM网络可以逐层扩展脑功率图谱中蕴含的概率分布信息,推理出更丰富的抽象特征信息,实现脑疲劳认知主题学习.对比其他脑疲劳检测方法,本文方法分类精度可提升2%.

The detection of a pilot’s brain fatigue state faces two important problems,which are how to generate a brain cognitive map and how to build a brain fatigue detection model.To solve the first problem,this paper uses the isometric azimuth projection to map brain fatigue indicators into a new type of brain power map.To solve the second problem,this work develops a deep latent Dirichlet model(DLDM),which solves the topic detection problem of pilot fatigue state.DLDM expands the probability distribution information contained in the developed brain power map layer by layer through multiple distributions,infers their hierarchical probability distribution characteristics,and gets more effective topic detection accuracy of pilot fatigue state.In order to avoid heuristic assumptions,this paper also proposes an effective stochastic gradient descent inference method with an adaptive learning rate between different layers and topics to more efficiently infer structure parameters of DLDM.The experimental results show that the DLDM can expand the probability distribution information of the brain power map layer by layer,infer richer abstract feature information,and detect brain fatigue cognitive topic.Compared with other brain fatigue detection methods,the classification accuracy of the proposed method can be improved by 2%.