We proposed a classification approach of stroke patients and healthy controls, as illustrated in Figure 1. The presented approach consisted of three main parts. (1) The preprocessing algorithm combining ICA and OEMD was used to extract independent source components from the 18-channel ERP signals. (2) Four types of features including P300 peak latency, P300 peak amplitude, RMS, and theta frequency band power were estimated, and they were differently chosen to compose a feature vector for further classification. (3) EMK-SVM was employed to perform the working memory task classification, and the classification accuracies were used to evaluate the performance of the proposed algorithm.
