Fig. 7.

The classification results by using different size of smoothed FB-CSP features and SVM for both “Dataset2a” and “Dataset2b”. To obtain the performance of classification influenced by the sequential relationships, a group of smoothing windows with the size range [0,4] is presented to FB-CSP features. In our experiments, via smoothed FB-CSP features, the SVM classifier is used for motor imagery classification. Among the results, “SW=0” expresses the FB-CSP features without smoothing. The size number of smoothing time window fully influences the performance of EEG signals classification. a The classification results of “Dataset2a” and b The classification results of “Dataset2b”