希尔伯特-黄变换
极限学习机
特征选择
粒子群优化
人工智能
样本熵
模式识别(心理学)
计算机科学
熵(时间箭头)
特征提取
脑电图
光谱密度
算法
人工神经网络
物理
计算机视觉
精神科
滤波器(信号处理)
电信
量子力学
心理学
作者
Yun Zheng,Yuliang Ma,Jared A Cammon,Songjie Zhang,Jianhai Zhang,Yingchun Zhang
标识
DOI:10.1016/j.compbiomed.2022.105718
摘要
This study aims to identify new electroencephalography (EEG) features for the detection of driving fatigue. The most common EEG feature in driving fatigue detection is the power spectral density (PSD) of five frequency bands, i.e., alpha, beta, gamma, delta, and theta bands. PSD has proved to be useful, however its flaw is that it covers much implicit information of the time domain. In this study we propose a new approach, which combines ensemble empirical mode decomposition (EEMD) and PSD, to explore new EEG features for driving fatigue detection. Through EEMD we get a series of intrinsic mode function (IMF) components, from which we can extract PSD features. We used six features to compare with the proposed features, including the PSD of five frequency bands, PSD of empirical mode decomposition (EMD)-IMF components, PSD, permutation entropy (PE), sample entropy (SE), and fuzzy entropy (FE) of EEMD-IMF components, and common spatial pattern. Feature overlap ratio and multiple machine learning methods were applied to evaluate these feature extraction approaches. The results show that the classification accuracy and overlap ratio of experiments based on IMF's energy spectrum is far superior to other features. Through channel optimization and a comparison of accuracy, we conclude that our new feature selection approach has a better performance based on the modified hierarchical extreme learning machine algorithm with Particle Swarm Optimization (PSO-H-ELM) classifier, which has the highest average accuracy of 97.53%.
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