In this study, 16-channel ERP signals were classified via a 5-fold cross-validation. First, the ERP data were partitioned into 5 equally sized folds. Second, 5 iterations of training and validation were performed such that within each iteration a different fold of the ERP data was held out for validation while the remaining 4 folds were used for learning. Finally, the classification results from 5 folds were averaged to produce the classification accuracy. Four types of features, including P300 peak latency, P300 peak amplitude, RMS, and theta frequency band power, were extracted for the classification using the EMK-SVM algorithm.
