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

Rong Liu,Yongxuan Wang,Xinyu Wu,Jun Cheng

doi:10.1155/2017/2948742

Abstract

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.

Highlights

Noninvasive brain-computer interface (BCI) based on the electroencephalogram (EEG) offers a new means of communication to locked-in or paralyzed patients [1, 2] and controlling a prosthesis [3, 4] without reliance on the usual neuromuscular pathways
For the purpose of benchmarking, we compared the classification accuracy (ACC), the mutual information (MI) with two benchmark feature extraction algorithms, and discrete Fourier transformation (DFT) and wavelet transforms (WT) based on the sequential Bayesian classifier [17, 18]
The classification accuracy (ACC) and mutual information (MI) of the three methods in consideration are listed in Table 1, where Avg. denotes the averaged indexes over all four subjects

Summary

Introduction

Noninvasive brain-computer interface (BCI) based on the electroencephalogram (EEG) offers a new means of communication to locked-in or paralyzed patients [1, 2] and controlling a prosthesis [3, 4] without reliance on the usual neuromuscular pathways. The critical challenge of BCI technology is to classify the brain signals and mental tasks accurately and fast. Various pattern recognition algorithms were used in BCI system to extract and classify EEG features. Event-related desynchronization/synchronization (ERD/ ERS) patterns of motor imagery are effective features for EEG-based BCI systems. A pattern recognition algorithm should be used to facilitate decoding “motor intent,” both to find subjectspecific EEG features that maximize the separation between the patterns generated by executing the mental tasks and to train classifiers that minimize the classification error rates of these specific patterns. Feature extraction for discrimination of left- and right-hand motor imagery EEG is usually based on EEG band power (BP). Inspired by the wavelet method, we introduce a new feature extraction method based on power projective bases to classify EEGs without constrain of wavelet forms

Methods

Results

Conclusion