前额单通道脑电信号的疲劳特征提取及分类算法  

Fatigue feature extraction and classification algorithm of forehead single-channel electroencephalography signals

作　　者：杨慧舟 刘云飞[1] 夏丽娟 YANG Huizhou;LIU Yunfei;XIA Lijuan(College of Information Science and Technology&College of Artificial Intelligence,Nanjing Forestry University,Nanjing 210037,P.R.China;CR/RIX1-AP,Bosch(China)Investment Ltd.,Shanghai 200335,P.R.China)

机构地区：[1]南京林业大学信息科学技术学院人工智能学院,南京210037 [2]博世(中国)投资有限公司中央研究院,上海200335

出　　处：《生物医学工程学杂志》2024年第4期732-741,共10页Journal of Biomedical Engineering

基　　金：国家重点研发计划(2017YFD0600904)。

摘　　要：针对前额单通道脑电信号特征提取能力不足,导致疲劳检测精度降低的问题,本文提出一种基于有监督对比学习的疲劳特征提取及分类算法。首先,通过经典模态分解对原始信号进行滤波,提高信噪比;其次,考虑到一维信号在信息表达上的局限性,利用有重叠采样将信号转换为二维结构,同时表达信号短期内和长期间变化;由深度可分离卷积构建特征提取网络,加速模型运算;最后,通过联合有监督对比损失与均方误差损失对模型进行全局优化。实验表明,该算法对三种疲劳状态分类的平均准确度可达75.80%,相较于其它先进算法均有较大幅度提高,显著提高了单通道脑电信号进行疲劳检测的准确性与可行性。本文研究为单通道脑电信号应用提供了有力支持,也为疲劳检测研究提供了新思路。Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography(EEG)signals is insufficient,which leads to decreased fatigue detection accuracy,a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed.Firstly,the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio.Secondly,considering the limitation of the onedimensional signal in information expression,overlapping sampling is used to transform the signal into a two-dimensional structure,and simultaneously express the short-term and long-term changes of the signal.The feature extraction network is constructed by depthwise separable convolution to accelerate model operation.Finally,the model is globally optimized by combining the supervised contrastive loss and the mean square error loss.Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%,which is greatly improved compared with other advanced algorithms,and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved.The results provide strong support for the application of single-channel EEG signals,and also provide a new idea for fatigue detection research.

关 键 词：单通道脑电信号 驾驶疲劳检测 特征提取 有监督对比学习 

分 类 号：TN911.7[电子电信—通信与信息系统] R318[电子电信—信息与通信工程]