Detection of alertness-related EEG signals based on decision fused BP neural network

2022 ◽  
Vol 74 ◽  
pp. 103479
Author(s):  
Meiyan Zhang ◽  
Dan Liu ◽  
Qisong Wang ◽  
Boqi Zhao ◽  
Ou Bai ◽  
...  
2011 ◽  
Vol 217-218 ◽  
pp. 1366-1371 ◽  
Author(s):  
Zhen Dong Mu ◽  
Jian Feng Hu

In the information era, information security becomes more important, the use of EEG as identification tool become more and more. In this paper, we used the subjects’ photos as stimulation. In order to obtain the Identification classifier of different subjects, we used AR model to convert the EEG signals from time domain into the frequency domain, used Fisher’ distance to extract the feature. Finally, we calculated the feature by using BP neural network. Through the analysis of the correct recognition rate, error recognition rate and false recognition rate, we achieved the purpose of using EEG as identification tool.


2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods


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