Joadder Md A Mannan, Myszewski Joshua J, Rahman Mohammad H, Wang Inga
1Department of Electrical, & Electronic Engineering, United International University, Dhaka, Bangladesh.
2Department of Biomedical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211 USA.
Health Inf Sci Syst. 2019 Aug 7;7(1):15. doi: 10.1007/s13755-019-0076-2. eCollection 2019 Dec.
Significant research has been conducted in the field of brain computer interface (BCI) algorithm development, however, many of the resulting algorithms are both complex, and specific to a particular user as the most successful methodology can vary between individuals and sessions. The objective of this study was to develop a simple yet effective method of feature selection to improve the accuracy of a subject independent BCI algorithm and streamline the process of BCI algorithm development. Over the past several years, several high precision features have been suggested by researchers to classify different motor imagery tasks. This research applies fourteen of these features as a feature pool that can be used as a reference for future researchers. Additionally, we look for the most efficient feature or feature set with four different classifiers that best differentiates several motor imagery tasks. In this work we have successfully employed a feature fusion method to obtain the best sub-set of features. We have proposed a novel computer aided feature selection method to determine the best set of features for distinguishing between motor imagery tasks in lieu of the manual feature selection that has been performed in past studies. The features selected by this method were then fed into a Linear Discriminant Analysis, K-nearest neighbor, decision tree, or support vector machine classifier for classification to determine the overall performance.
The methods used were a novel performance based additive feature fusion algorithm working in conjunction with machine learning in order to classify the motor imagery signals into particular states. The data used for this study was collected from BCI competition III dataset IVa.
The result of this algorithm was a classification accuracy of 99% for a subject independent algorithm with less computation cost compared to traditional methods, in addition to multiple feature/classifier combinations that outperform current subject independent methods.
The conclusion of this study and its significance is that it developed a viable methodology for simple, efficient feature selection and BCI algorithm development, which leads to an overall increase in algorithm classification accuracy.
在脑机接口（BCI）算法开发领域已开展了大量研究，然而，许多生成的算法既复杂，又特定于某个用户，因为最成功的方法在个体和不同实验环节之间可能会有所不同。本研究的目的是开发一种简单而有效的特征选择方法，以提高独立于个体的BCI算法的准确性，并简化BCI算法的开发过程。在过去几年中，研究人员提出了几种高精度特征来对不同的运动想象任务进行分类。本研究应用其中的14种特征作为特征库，可供未来研究人员参考。此外，我们使用四种不同的分类器寻找最有效的特征或特征集，以最佳区分几种运动想象任务。在这项工作中，我们成功采用了一种特征融合方法来获得最佳特征子集。我们提出了一种新颖的计算机辅助特征选择方法，以确定用于区分运动想象任务的最佳特征集，取代过去研究中所采用的手动特征选择。然后将通过该方法选择的特征输入到线性判别分析、K近邻、决策树或支持向量机分类器中进行分类，以确定整体性能。
所使用的方法是一种基于性能的新型加法特征融合算法，与机器学习相结合，以便将运动想象信号分类到特定状态。本研究使用的数据来自BCI竞赛III数据集IVa。
该算法的结果是，对于独立于个体的算法，分类准确率达到99%，与传统方法相比计算成本更低，此外还有多个特征/分类器组合优于当前独立于个体的方法。
本研究的结论及其意义在于，它开发了一种可行的方法，用于简单、高效的特征选择和BCI算法开发，从而使算法分类准确率总体提高。
