Martinez-Leon Juan-Antonio, Cano-Izquierdo Jose-Manuel, Ibarrola Julio
Universidad Politécnica de Cartagena, Campus Muralla del Mar, Calle Doctor Fleming S/N, 30202 Cartagena, Spain.
Comput Intell Neurosci. 2015;2015:781207. doi: 10.1155/2015/781207. Epub 2015 Apr 21.
This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate. This way, it is possible to build cheaper, quicker, and more portable BCI systems. The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest. Furthermore, a new three-step methodology has been developed which includes a feature discriminant character calculation stage; a score, order, and selection phase; and a final feature selection step. For the first stage, both statistics method and fuzzy criteria are used. The fuzzy criteria are based on the S-dFasArt classification algorithm which has shown excellent performance in previous papers undertaking the BCI multiclass motor imagery problem. The score, order, and selection stage is used to sort the features according to their discriminant nature. Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.
本文介绍了一项旨在大幅减轻基于脑电图（EEG）的运动想象脑机接口（BCI）系统所需处理负担的研究。在本研究中，重点已从通道范式转向特征范式，并且在保持甚至提高分类成功率的同时，实现了该过程所需特征数量减少96%。通过这种方式，可以构建更便宜、更快且更便携的BCI系统。所使用的数据集是在BCI竞赛III的框架内提供的，这使得能够将所呈现的结果与竞赛中所达到的分类准确率进行比较。此外，还开发了一种新的三步方法，该方法包括一个特征判别特征计算阶段、一个评分、排序和选择阶段以及一个最终的特征选择步骤。在第一阶段，同时使用了统计方法和模糊准则。模糊准则基于S-dFasArt分类算法，该算法在先前处理BCI多类运动想象问题的论文中已表现出优异的性能。评分、排序和选择阶段用于根据特征的判别性质对其进行排序。最后，同时使用顺序选择和数据处理分组方法（GMDH）来选择最具判别力的特征。
