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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 2948742, 10 pages
Research Article

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

1Biomedical Engineering Department, Dalian University of Technology, Dalian, Liaoning 116024, China
2Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning 116001, China
3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Correspondence should be addressed to Yongxuan Wang; nc.ude.tuld.liam@4098xyw

Received 30 June 2017; Accepted 11 September 2017; Published 14 November 2017

Academic Editor: Fei Chen

Copyright © 2017 Rong Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.