In this research, we study the relationship between mental workload and facial temperature of aircraft participants during a simulated takeoff flight. We conducted experiments to comprehend the correlation between wor...In this research, we study the relationship between mental workload and facial temperature of aircraft participants during a simulated takeoff flight. We conducted experiments to comprehend the correlation between work and facial temperature within the flight simulator. The experiment involved a group of 10 participants who played the role of pilots in a simulated A-320 flight. Six different flying scenarios were designed to simulate normal and emergency situations on airplane takeoff that would occur in different levels of mental workload for the participants. The measurements were workload assessment, face temperatures, and heart rate monitoring. Throughout the experiments, we collected a total of 120 instances of takeoffs, together with over 10 hours of time-series data including heart rate, workload, and face thermal images and temperatures. Comparative analysis of EEG data and thermal image types, revealed intriguing findings. The results indicate a notable inverse relationship between workload and facial muscle temperatures, as well as facial landmark points. The results of this study contribute to a deeper understanding of the physiological effects of workload, as well as practical implications for aviation safety and performance.展开更多
The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human ...The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.展开更多
Objective This paper presents classifications of m ental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) n etwork with optimal centers and widths for the Brain-Computer Interface (BCI) s che...Objective This paper presents classifications of m ental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) n etwork with optimal centers and widths for the Brain-Computer Interface (BCI) s chemes. Methods Initial centers and widths of the network are s elected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during train ing phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three t ask pairs over four subjects achieves 87.0%. Moreover, this network runs fast du e to the fewer hidden layer neurons. Conclusion The adaptive RB F network with optimal centers and widths has high recognition rate and runs fas t. It may be a promising classifier for on-line BCI scheme.展开更多
Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved ...Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved in goal-directed behavior. Extensive research has been done to develop methods for recognizing physiological and psychophysiological signs of mental fatigue. This has allowed the development of many AI-based models to classify different levels of fatigue, using data extracted from eye-tracking device, EEG, or ECG. In this paper, we present an experimental protocol which aims to both generate/measure mental fatigue and provide effective strategies for recuperation via VR sessions paired with EEG and eye tracking devices. This paper first provides a comprehensive state-of-the-art of mental fatigue predictive factors, measurement methods, and recuperation strategies. Then the paper presents an experimental protocol resulting from the state-of-the-art to 1) generate and measure mental fatigue and 2) evaluate the effectiveness of virtual therapy for fatigue recuperation, using a virtual reality (VR) simulated environment. In our work, we successfully generated mental fatigue through completion of cognitive tasks in a virtual simulated environment. Participants showed significant decline in pupil diameter and theta/alpha score during the various cognitive tasks. We trained an RBF SVM classifier from Electroencephalogram (EEG) data classifying mental fatigue with 95% accuracy on the test set. Finally, our results show that the time allocated for virtual therapy did not improve pupil diameter in post-relaxation period. Further research on the impact of relaxation therapy on relaxation therapy should allocate time closer to the standard recovery time of 60 min.展开更多
脑力负荷识别对提高人机交互操作人员的工作绩效具有重要意义。目前已有研究表明,通过提取脑电(electroencephalogram,EEG)信号的能量特征进行脑力负荷识别取得了不错的分类效果。但该方法只关注到信号的幅度信息,而忽略了相位信息;只...脑力负荷识别对提高人机交互操作人员的工作绩效具有重要意义。目前已有研究表明,通过提取脑电(electroencephalogram,EEG)信号的能量特征进行脑力负荷识别取得了不错的分类效果。但该方法只关注到信号的幅度信息,而忽略了相位信息;只分析了各个通道的频域特征,没有考虑不同通道信号之间的同步关系。为充分考虑不同脑区间的功能连接性,提出一种基于加权相位滞后指数(weighted phase lag index,WPLI)热力图的脑力负荷分类方法。对预处理后的脑电信号计算两两通道间的WPLI并绘制热力图,用于评估不同通道信号之间的相位耦合情况,由此反映不同脑区间的功能连接性。由WPLI热力图可以直观地观察到:在高、低负荷状态下,大脑功能连接性的分布存在明显差异。通过实验分别对能量特征图和WPLI热力图采用方向梯度直方图-支持向量机(histogram of oriented gradient-support vector machine,HOG-SVM)和LeNet-5两种方法进行分类。结果表明:WPLI热力图和LeNet-5的组合具有较好的分类结果。展开更多
文摘In this research, we study the relationship between mental workload and facial temperature of aircraft participants during a simulated takeoff flight. We conducted experiments to comprehend the correlation between work and facial temperature within the flight simulator. The experiment involved a group of 10 participants who played the role of pilots in a simulated A-320 flight. Six different flying scenarios were designed to simulate normal and emergency situations on airplane takeoff that would occur in different levels of mental workload for the participants. The measurements were workload assessment, face temperatures, and heart rate monitoring. Throughout the experiments, we collected a total of 120 instances of takeoffs, together with over 10 hours of time-series data including heart rate, workload, and face thermal images and temperatures. Comparative analysis of EEG data and thermal image types, revealed intriguing findings. The results indicate a notable inverse relationship between workload and facial muscle temperatures, as well as facial landmark points. The results of this study contribute to a deeper understanding of the physiological effects of workload, as well as practical implications for aviation safety and performance.
文摘The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.
基金ThisworkwassupportedbytheNationalNaturalScienceFoundationofChina (No .3 0 3 70 3 95 )
文摘Objective This paper presents classifications of m ental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) n etwork with optimal centers and widths for the Brain-Computer Interface (BCI) s chemes. Methods Initial centers and widths of the network are s elected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during train ing phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three t ask pairs over four subjects achieves 87.0%. Moreover, this network runs fast du e to the fewer hidden layer neurons. Conclusion The adaptive RB F network with optimal centers and widths has high recognition rate and runs fas t. It may be a promising classifier for on-line BCI scheme.
文摘Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved in goal-directed behavior. Extensive research has been done to develop methods for recognizing physiological and psychophysiological signs of mental fatigue. This has allowed the development of many AI-based models to classify different levels of fatigue, using data extracted from eye-tracking device, EEG, or ECG. In this paper, we present an experimental protocol which aims to both generate/measure mental fatigue and provide effective strategies for recuperation via VR sessions paired with EEG and eye tracking devices. This paper first provides a comprehensive state-of-the-art of mental fatigue predictive factors, measurement methods, and recuperation strategies. Then the paper presents an experimental protocol resulting from the state-of-the-art to 1) generate and measure mental fatigue and 2) evaluate the effectiveness of virtual therapy for fatigue recuperation, using a virtual reality (VR) simulated environment. In our work, we successfully generated mental fatigue through completion of cognitive tasks in a virtual simulated environment. Participants showed significant decline in pupil diameter and theta/alpha score during the various cognitive tasks. We trained an RBF SVM classifier from Electroencephalogram (EEG) data classifying mental fatigue with 95% accuracy on the test set. Finally, our results show that the time allocated for virtual therapy did not improve pupil diameter in post-relaxation period. Further research on the impact of relaxation therapy on relaxation therapy should allocate time closer to the standard recovery time of 60 min.
文摘脑力负荷识别对提高人机交互操作人员的工作绩效具有重要意义。目前已有研究表明,通过提取脑电(electroencephalogram,EEG)信号的能量特征进行脑力负荷识别取得了不错的分类效果。但该方法只关注到信号的幅度信息,而忽略了相位信息;只分析了各个通道的频域特征,没有考虑不同通道信号之间的同步关系。为充分考虑不同脑区间的功能连接性,提出一种基于加权相位滞后指数(weighted phase lag index,WPLI)热力图的脑力负荷分类方法。对预处理后的脑电信号计算两两通道间的WPLI并绘制热力图,用于评估不同通道信号之间的相位耦合情况,由此反映不同脑区间的功能连接性。由WPLI热力图可以直观地观察到:在高、低负荷状态下,大脑功能连接性的分布存在明显差异。通过实验分别对能量特征图和WPLI热力图采用方向梯度直方图-支持向量机(histogram of oriented gradient-support vector machine,HOG-SVM)和LeNet-5两种方法进行分类。结果表明:WPLI热力图和LeNet-5的组合具有较好的分类结果。
