Error Exponent Sensitivity of Sequential Probability Ratio Testing

2021 ◽  
Author(s):  
Parham Boroumand ◽  
Albert Guillen i Fabregas
Keyword(s):  
Error Exponent ◽  
Probability Ratio ◽  
Sequential Probability ◽  
Sequential Probability Ratio Testing

The Application of Sequential Probability Ratio Testing to a Test of Motor Skill

1985 ◽  
Vol 56 (1) ◽  
pp. 58-65 ◽  
Author(s):  
Margaret J. Safrit ◽  
Terry M. Wood ◽  
Sara A. Ehlert ◽  
Linda M. Hooper ◽  
Patricia Patterson
Keyword(s):  
Motor Skill ◽  
Probability Ratio ◽  
Sequential Probability ◽  
Sequential Probability Ratio Testing

scholarly journals Evaluation of an automated safety surveillance system using risk adjusted sequential probability ratio testing

2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael E Matheny ◽  
Sharon-Lise T Normand ◽  
Thomas P Gross ◽  
Danica Marinac-Dabic ◽  
Nilsa Loyo-Berrios ◽  
...  
Keyword(s):  
Surveillance System ◽  
Probability Ratio ◽  
Safety Surveillance ◽  
Sequential Probability ◽  
Sequential Probability Ratio Testing

scholarly journals Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Rong Liu ◽  
Yongxuan Wang ◽  
Xinyu Wu ◽  
Jun Cheng
Keyword(s):  
Decision Making ◽  
Stopping Time ◽  
Probability Ratio ◽  
Computer Interfaces ◽  
Average Maximum ◽  
Base Method ◽  
Sequential Probability ◽  
Time Accuracy ◽  
Decision Making Model ◽  
Sequential Probability Ratio Testing

Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with a power projective base method. Then we were applied a decision-making model, the sequential probability ratio testing (SPRT), for single-trial classification of motor imagery movement events. The unique strength of this proposed classification method lies in its accumulative process, which increases the discriminative power as more and more evidence is observed over time. The properties of the method were illustrated on thirteen subjects’ recordings from three datasets. Results showed that our proposed power projective method outperformed two benchmark methods for every subject. Moreover, with sequential classifier, the accuracies across subjects were significantly higher than that with nonsequential ones. The average maximum accuracy of the SPRT method was 84.1%, as compared with 82.3% accuracy for the sequential Bayesian (SB) method. The proposed SPRT method provides an explicit relationship between stopping time, thresholds, and error, which is important for balancing the time-accuracy trade-off. These results suggest SPRT would be useful in speeding up decision-making while trading off errors in BCI.

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