估计员
相关性
格兰杰因果关系
任务(项目管理)
计算机科学
偏相关
连贯性(哲学赌博策略)
皮尔逊积矩相关系数
人工智能
统计
心理学
数学
机器学习
工程类
几何学
系统工程
作者
Dulan Perera,Yu–Kai Wang,Chin‐Teng Lin,Jinchuan Zheng,Hung T. Nguyen,Rifai Chai
出处
期刊:International Conference of the IEEE Engineering in Medicine and Biology Society
日期:2020-07-01
卷期号:: 3208-3211
被引量:5
标识
DOI:10.1109/embc44109.2020.9176240
摘要
This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves. Granger-Geweke causality (GGC), directed transfer function (DTF) and partial directed coherence (PDC) brain connectivity estimation methods were used in brain connectivity analysis. Correlation test and a student's t-test were conducted on the connectivity matrixes. Results show a significant difference between the mean of distracted drivers and non-distracted driver's brain connectivity matrixes. GGC and DTF methods student's t-tests shows a p-value below 0.05 with the correlation coefficients varying from 0.62 to 0.38. PDC connectivity estimation method does not show a significant difference between the connectivity matrixes means unless it is compared with lane keeping task and the normal driving task. Furthermore, it shows a strong positive correlation between the connectivity matrixes.
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