In this paper, we presented the EMK-SVM algorithm for ERP-based signal classification for stroke patients and healthy controls with four features (i.e., P300 peak latency, P300 peak amplitude, RMS, and theta frequency band power). The proposed method had better performance than other typical methods (i.e., QDA and LDA). It achieved above 78.4% accuracy for 0-back task and above 75% for 1-back task. The statistical test results showed that the differences of selected features were significant. Therefore, it provides a powerful tool to assess cognitive function.
