Abstract：
OBJECTIVE: Operator\'s capability for accurately comprehending verbal commands is critically important to maintain the performance of human-machine interaction. It can be evaluated by human mental workload measured with electroencephalography (EEG). However, the time duration of different workload conditions within a task session is unequal due to varied psychophysiological processes across individuals. It leads to data imbalance of the EEG for training workload classifiers.
METHODS: In this study, we propose an EEG feature oversampling technique, Gaussian-SMOTE based feature ensemble (GSMOTE-FE), for workload recognition with imbalanced classes. First, artificial EEG instances are drawn from a Gaussian distribution in the margin between the minority and majority workload classes. Tomek links are detected as clues to remove redundant feature vectors. Then, we embed a feature selection module based on the GINI importance while an ensemble classifier committee with bootstrap aggregating is used to further enhance classification performance.
RESULTS: We validate the GSMOTE-FE framework based on an experiment that simulates operators to understand the correct meaning of the instructions in the Chinese language. Participants\' EEG signals and reaction time data were both recorded to validate the proposed workload classifier. Workload classification accuracy and Macro-F1 values are 0.6553 and 0.5862, respectively. Corresponding G-mean and AUC achieve at 0.5757 and 0.5958, respectively.
CONCLUSIONS: The performance of the GSMOTE-FE is demonstrated to be comparable with the advanced oversampling techniques. The workload classifier has the capability to indicate low and high levels of the task demand of the Chinese language understanding task.
METHODS: In this study, we propose an EEG feature oversampling technique, Gaussian-SMOTE based feature ensemble (GSMOTE-FE), for workload recognition with imbalanced classes. First, artificial EEG instances are drawn from a Gaussian distribution in the margin between the minority and majority workload classes. Tomek links are detected as clues to remove redundant feature vectors. Then, we embed a feature selection module based on the GINI importance while an ensemble classifier committee with bootstrap aggregating is used to further enhance classification performance.
RESULTS: We validate the GSMOTE-FE framework based on an experiment that simulates operators to understand the correct meaning of the instructions in the Chinese language. Participants\' EEG signals and reaction time data were both recorded to validate the proposed workload classifier. Workload classification accuracy and Macro-F1 values are 0.6553 and 0.5862, respectively. Corresponding G-mean and AUC achieve at 0.5757 and 0.5958, respectively.
CONCLUSIONS: The performance of the GSMOTE-FE is demonstrated to be comparable with the advanced oversampling techniques. The workload classifier has the capability to indicate low and high levels of the task demand of the Chinese language understanding task.
摘要：
目标：操作员准确理解口头命令的能力对于保持人机交互的性能至关重要。可以通过脑电图（EEG）测量的人类心理工作量来评估。然而,由于个体之间不同的心理生理过程，任务会话中不同工作量条件的持续时间是不相等的。这导致用于训练工作量分类器的EEG的数据不平衡。
方法：在本研究中，我们提出了一种EEG特征过采样技术，基于高斯-SMOTE的特征集合(GSMOTE-FE)，用于不平衡班级的工作量识别。首先,人工EEG实例是从少数和多数工作量类别之间的高斯分布中得出的。Tomek链接被检测为删除冗余特征向量的线索。然后，我们嵌入了一个基于GINI重要性的特征选择模块，而一个带有引导聚合的集成分类器委员会用于进一步提高分类性能。
结果：我们基于一项实验来验证GSMOTE-FE框架，该实验模拟操作员以理解中文语言中说明的正确含义。记录参与者的EEG信号和反应时间数据，以验证所提出的工作量分类器。工作负载分类精度和宏F1值分别为0.6553和0.5862。相应的G-平均值和AUC分别达到0.5757和0.5958。
结论：证明GSMOTE-FE的性能与先进的过采样技术相当。工作量分类器具有指示中文语言理解任务的任务需求的低水平和高水平的能力。
方法：在本研究中，我们提出了一种EEG特征过采样技术，基于高斯-SMOTE的特征集合(GSMOTE-FE)，用于不平衡班级的工作量识别。首先,人工EEG实例是从少数和多数工作量类别之间的高斯分布中得出的。Tomek链接被检测为删除冗余特征向量的线索。然后，我们嵌入了一个基于GINI重要性的特征选择模块，而一个带有引导聚合的集成分类器委员会用于进一步提高分类性能。
结果：我们基于一项实验来验证GSMOTE-FE框架，该实验模拟操作员以理解中文语言中说明的正确含义。记录参与者的EEG信号和反应时间数据，以验证所提出的工作量分类器。工作负载分类精度和宏F1值分别为0.6553和0.5862。相应的G-平均值和AUC分别达到0.5757和0.5958。
结论：证明GSMOTE-FE的性能与先进的过采样技术相当。工作量分类器具有指示中文语言理解任务的任务需求的低水平和高水平的能力。
