Novel Nonlinear Approach for Real-Time Fatigue EEG Data: An Infinitely Warped Model of Weighted Permutation Entropy
Novel Nonlinear Approach for Real-Time Fatigue EEG Data: An Infinitely Warped Model of Weighted Permutation Entropy
复制标题
用于实时疲劳脑电图数据的新型非线性方法：加权排列熵的无限扭曲模型
DOI：
10.1109/tits.2019.2918438
复制
发表时间：
2020-06-01
影响因子：
8.5
通讯作者：
Zhou, Gui-Rong
中科院分区：
文献类型：
--
作者：
Wu, Edmond Q.;Zhu, Li-Min;Zhou, Gui-Rong
工作量评估面临两个主要问题。即如何学习有效的疲劳特性，如何发现工作负荷的潜在状态。本文提出了一种利用瞬时谱熵特征和无限弯曲模型来评估飞行员脑疲劳负荷的方法。利用希尔伯特变换提取脑电图信号的瞬时特征，并提出欧几里得范数加权排列熵。无限弯曲模型是一种新的用于检测任意形状脑电数据的自动学习模型。此外，我们提出了一个快速学习框架，通过集成Treelet变换和无限弯曲模型来学习心理疲劳。与其他最先进的方法相比，我们的方法能够更好地处理复杂形状的复杂数据。实验结果表明，该方法能更有效地评估飞行员的脑疲劳状况。
Workload assessment faces two major issues. That is, how to learn effective fatigue characteristics and how to find the potential state of the workload. This paper proposes a solution to assess the brain fatigue workload of pilots through an instantaneous spectral entropy feature and an infinitely warped model. The instantaneous characteristics of electroencephalography (EEG) signals are extracted by Hilbert transform, and Euclidean norm weighted permutation entropy is proposed. The infinitely warped model is a new automatic learning model for detecting arbitrary shapes of EEG data. In addition, we propose a rapid learning framework to learn mental fatigue by integrating Treelet transform and infinitely warped models. Compared to other state-of-the-art methods, our approach is better able to handle complex data in complex shapes. The experimental results show that this method can more effectively assess the brain fatigue of pilots.