Classification of Pilots'Mental Workload based on Multi-Physiological Signal Fusion
The paper presents an efficient decision-making model for categorizing pilots'mental workload by employing a neural network model combining the mechanism of multi-head attention for the multi-modal data fusion of Electroencephalogram,Electrocardiogram,and Electromyography.The findings confirmed the effectiveness of multiple bioelectrical signal fusion in the mental workload state classification method.The method proposed in this paper exhibits an accuracy of 96.54％,improved by 10.11％compared to the previous studies on single physiological signals and by 2.32％compared to the previous studies on multi-physiological signals decision-making.This study provides a useful reference for promoting the study of aviation safety.
万方数据
维普
