基于样本选择的跨被试脑力负荷识别.

The degree of mental workload is closely related to the work efficiency and the allocation of human resources and the occurrence of accidents of man-machine operation. Therefore, it is of great significance to study the state of operators􀆳 mental workload. In order to solve the problem that the classification effect of the existing mental workload recognition methods is poor due to the small number of samples in the training set, a cross-subject mental workload classification method based on instance selection for visual and operational task was proposed. Firstly, referring to a small amount of historical data of the target subject, an adaptive source domain training set was obtained by instance selection of the training set data of the electroencephalogram (EEG) data of other subjects, thereby the number of samples and the domain difference between the training sets and test sets was reduced. Secondly, principal component analysis was used to reduce the feature dimension of the adaptive training set and target test set. Finally, the adaptive training set principal component was used to establish a support vector machine classification model to identify the mental workload state of the test set samples. The results show that this method can improve classification efficiency while improving classification accuracy, and achieve fast and accurate mental workload state recognition. [ABSTRACT FROM AUTHOR]