LAUSR

In a motor imagery-based brain–computer interface system, an effective classifier is required. However, the effectiveness of classifier is substantially influenced by the individual differences among electroencephalography (EEG) signals and artifacts. Therefore, in this study, we adopted an ensemble method by combining various classifiers, including a fuzzy classifier that can reduce the influence of artifacts, to improve the robustness and accuracy in classification across participants. Nine participants were recruited for the experiment and asked to perform a left- and right-hand motor imagery task. We calculated the classification rates obtained with the linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Naive Bayes, support vector machine (SVM), and fuzzy twin SVM (FTSVM) classifiers based on the spectral features extracted by an autoregressive (AR) model and the spectral–temporal features extracted by the Morlet wavelet from overlapped 1.024-s EEG segments. The fivefold cross-validation accuracies of the ensemble method for the 1.024-s EEG were 71.39% and 73.06% with the AR- and wavelet-extracted features, respectively. In the comparison of individual classifiers, the Linear-FTSVM method outperformed other individual classifiers. In addition, the ensemble model with the inclusion of FTSVM classifiers performs superior to the ensemble models without using FTSVM classifiers.