Nonparametric Hierarchical Hidden Semi-Markov Model for Brain Fatigue Behavior Detection of Pilots During Flight
Nonparametric Hierarchical Hidden Semi-Markov Model for Brain Fatigue Behavior Detection of Pilots During Flight
复制标题
飞行中飞行员脑疲劳行为检测的非参数分层隐半马尔可夫模型
DOI：
10.1109/tits.2021.3052801
复制
发表时间：
2021-02-02
影响因子：
8.5
通讯作者：
Zhou, Gui-Rong
中科院分区：
文献类型：
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作者：
Wu, Edmond Q.;Zhu, Li-Min;Zhou, Gui-Rong
飞行员大脑活动的评估对飞行安全非常重要。本研究提出了一种具有分层先验的隐式半马尔可夫模型来检测不同飞行任务下的大脑活动。提出了一种动态的学生混合模型来检测HSMM发射概率的异常值。还可以从脑电信号中提取瞬时谱特征。与其他潜变量模型相比，该模型对飞行员大脑认知活动的自动推理具有良好的性能。结果表明，分层模型的考虑和混合模型的发射概率提高了对飞行员疲劳认知水平的识别性能。
The evaluation of pilot brain activity is very important for flight safety. This study proposes a Hidden semi-Markov Model with Hierarchical prior to detect brain activity under different flight tasks. A dynamic student mixture model is proposed to detect the outlier of emission probability of HSMM. Instantaneous spectrum features are also extracted from EEG signals. Compared with other latent variable models, the proposed model shows excellent performance for the automatic inference of brain cognitive activity of pilots. The results indicate that the consideration of hierarchical model and the emission probability with ${t}$ mixture model improves the recognition performance for Pilots’ fatigue cognitive level.