*2.7. Machine Learning Classification Technique*

To decode the subjects' limb movement intent from the constructed feature matrix, two machine learning based classification algorithms including support vector machine (SVM) and linear discriminant analysis (LDA) were utilized. Meanwhile, five-fold cross validation technique was employed for the partitioning of the extracted feature matrix into training and testing sets. The rationale behind considering these classification schemes is that their performances are relatively good, especially when considering multi-class problems [1,10,36]. Therefore, we built an SVM classifiers driven by radial basis function, and compared its classification performance with that of the LDA classifiers. Notably, we found that SVM achieved an overall accuracy that is slightly lower in comparison to the LDA. Meanwhile, the LDA classification scheme runs much faster than its SVM counterpart. Also due

to its relatively simple structure, and easy implemented in real-time, it was adopted in the current study [6,8,10].
