A Study on EEG-based Operational Fatigue Prediction for Video Investigators
The study is to explore the fatigue status of video investigators and provide fatigue monitoring methods.By collecting the electroencephalogram(EEG)data and subjective questionnaire data of video scouts,a fatigue prediction system based on BP neural network model was established.A total of 12 video investigators were recruited as subjects and their EEG signals were recorded using EEG devices when performing specific reconnaissance tasks.The EEG eigenvalues of α/β,θ/β,(α+θ)/β and(α+θ)/(α+β)were extracted as physiological indicators for fatigue detection,and combined with the subjective scale data,a BP neural network model was constructed to predict the fatigue state of video scouts before and after work.The experimental results show that the eigenvalues of four significant channels are identified by pairing t-test of the eigenvalues of all channels,and these eigenvalues show high accuracy in pre-dicting the fatigue state of video scouts.Based on these eigenvalues,the fitting accuracy of the established BP neural network model in predicting the fatigue state of video scouts is 84.348%.The fatigue monitoring method proposed in this study not only provides a scien-tific basis for the work scheduling of video investigators,but also helps to improve their decision-making efficiency at work.In addition,this method also provides a technical means and theoretical basis for the research and development of related fatigue detection technolo-gies,which is of great significance for improving the efficiency and quality of the work in the field of public safety.
video investigatorsoperational fatigueEEGBP neural network
万方数据
维普
