In this study, we first applied the algorithm combining ICA and OEMD to the ERP data of stroke patients and healthy controls and used four types of features including P300 peak latency, P300 peak amplitude, root mean square (RMS), and theta frequency band power to separate stroke patients from healthy ones. Then the features and the evolutionary multiple kernel SVM (EMK-SVM) based on genetic programming (GP) were used to perform the recognition of stroke patients and healthy controls based on working memory tasks. These tasks that may elicit a P300 ERP component were 0-back and 1-back tasks.
