There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantified.Therefore,EEG signals...There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantified.Therefore,EEG signals are introduced in this paper to investigate the evaluation and design method of vehicle acceleration sound with powerful sound quality.Firstly,the experiment of EEG acquisition and subjective evaluation under the stimulation of powerful vehicle sounds is conducted,respectively,then three physiological EEG features of PSD_β,PSD_γand DE are constructed to evaluate the vehicle sounds based on the correlation analysis algorithms.Subsequently,the Adaptive Genetic Algorithm(AGA)is proposed to optimize the Elman model,where an intelligent model(AGA–Elman)is constructed to objectively predicate the perception of subjects for the vehicle sounds with powerful sound quality.The results demonstrate that the error of the constructed AGA–Elman model is only 2.88%,which outperforms than the traditional BP and Elman model;Finally,two vehicle acceleration sounds(Design1 and Design2)are designed based on the constructed AGA–Elman model from the perspective of order modulation and frequency modulation,which provide the acoustic theoretical guidance for the design of vehicle sound incorporating the EEG signals.展开更多
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
脑电图(Electroencephalography,EEG)可记录来自大脑皮层的电信息,反映了脑活动中神经细胞放电产生的电场变化情况.EEG的空间信息和时间信息对于运动想象脑电(Motor Imagery Electroencephalogram,MI-EEG)解码分类模型学习判别特征至关...脑电图(Electroencephalography,EEG)可记录来自大脑皮层的电信息,反映了脑活动中神经细胞放电产生的电场变化情况.EEG的空间信息和时间信息对于运动想象脑电(Motor Imagery Electroencephalogram,MI-EEG)解码分类模型学习判别特征至关重要,但过度依赖预处理和手工特征提取,导致对EEG数据进行信号分类较为困难.尽管深度学习已经在很多领域实现了自动特征提取,但脑电图的深度学习尚未完成.提出基于FBCSP (Filter Bank Common Spatial Patterns)和Transformer模型的时空特征学习的运动想象脑电解码方法.针对FBCSP滤波的脑电信号,依次通过空间维度和时间维度上的注意力转换来获取空间和时间特征,然后通过Softmax函数对不同类别的EEG数据进行分类.实验结果表明,在BCI竞赛数据集IV-2a上,该方法的分类准确率可达84.16%,为MI脑电信号分类提供了新思路.展开更多
基金supported by the National Natural Science Foundation of China(No.52272389)the National Natural Science Foundation of China(No.52175111).
文摘There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantified.Therefore,EEG signals are introduced in this paper to investigate the evaluation and design method of vehicle acceleration sound with powerful sound quality.Firstly,the experiment of EEG acquisition and subjective evaluation under the stimulation of powerful vehicle sounds is conducted,respectively,then three physiological EEG features of PSD_β,PSD_γand DE are constructed to evaluate the vehicle sounds based on the correlation analysis algorithms.Subsequently,the Adaptive Genetic Algorithm(AGA)is proposed to optimize the Elman model,where an intelligent model(AGA–Elman)is constructed to objectively predicate the perception of subjects for the vehicle sounds with powerful sound quality.The results demonstrate that the error of the constructed AGA–Elman model is only 2.88%,which outperforms than the traditional BP and Elman model;Finally,two vehicle acceleration sounds(Design1 and Design2)are designed based on the constructed AGA–Elman model from the perspective of order modulation and frequency modulation,which provide the acoustic theoretical guidance for the design of vehicle sound incorporating the EEG signals.
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.
文摘脑电图(Electroencephalography,EEG)可记录来自大脑皮层的电信息,反映了脑活动中神经细胞放电产生的电场变化情况.EEG的空间信息和时间信息对于运动想象脑电(Motor Imagery Electroencephalogram,MI-EEG)解码分类模型学习判别特征至关重要,但过度依赖预处理和手工特征提取,导致对EEG数据进行信号分类较为困难.尽管深度学习已经在很多领域实现了自动特征提取,但脑电图的深度学习尚未完成.提出基于FBCSP (Filter Bank Common Spatial Patterns)和Transformer模型的时空特征学习的运动想象脑电解码方法.针对FBCSP滤波的脑电信号,依次通过空间维度和时间维度上的注意力转换来获取空间和时间特征,然后通过Softmax函数对不同类别的EEG数据进行分类.实验结果表明,在BCI竞赛数据集IV-2a上,该方法的分类准确率可达84.16%,为MI脑电信号分类提供了新思路.
