title: An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task.
abstract: Mental workload (MW)-based adaptive system has been found to be an effective approach to enhance the performance of human-machine interaction and to avoid human error caused by overload. However, MW estimated from the spontaneously generated electroencephalogram (EEG) was found to be task-specific. In existing studies, EEG-based MW classifier can work well under the task used to train the classifier (within-task) but crash completely when used to classify MW of a task that is similar to but not included in the training data (cross-task). The possible causes have been considered to be the task-specific EEG patterns, the mismatched workload across tasks and the temporal effects. In this study, cross-task performance-based feature selection (FS) and regression model were tried to cope with these challenges, in order to make EEG-based MW estimator trained on working memory tasks work well under a complex simulated multi-attribute task (MAT). The results show that the performance of regression model trained on working memory task and tested on multi-attribute task with the feature subset picked-out were significantly improved (correlation coefficient (COR): 0.740 ± 0.147 and 0.598 ± 0.161 for FS data and validation data respectively) when compared to the performance in the same condition with all features (chance level). It can be inferred that there do exist some MW-related EEG features can be picked out and there are something in common between MW of a relatively simple task and a complex task. This study provides a promising approach to measure MW across tasks.
authors.affiliation: Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University Tianjin, China.
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Ke
authors.affiliation: Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University Tianjin, China.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Qi
authors.affiliation: Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University Tianjin, China.
authors.forename: Feng
authors.initials: F
authors.lastname: He
authors.affiliation: Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University Tianjin, China.
authors.forename: Shuang
authors.initials: S
authors.lastname: Liu
authors.affiliation: Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University Tianjin, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhao
authors.affiliation: Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University Tianjin, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhou
authors.affiliation: Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University Tianjin, China.
authors.forename: Lixin
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Laboratory of Neural Engineering and Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University Tianjin, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Ming
keywords: EEG; cross-task; feature selection; mental workload; multi-attribute task; passive brain computer-interface; working memory task