## 4 Discussion  
_Comparison between Classifiers Used for BCI and Our Method_  
In order to be able to compare the results of the classifiers used in this study we made the graphs in Figures 19 and 20 corresponding to the subjects performing tasks of motor imagery of the right hand tasks of motor imagery of the left hand respectively. In Figure 18 it can be observed that most of the maximum values of the classification of 94% are obtained with the LDA classifier based on ICA but also with this classifier for six situations the lowest values of the classification are obtained. From here we can conclude that this method is quite sensitive to being used for the implementation of a brain-computer interface. Moreover 95.67% is obtained when using the hybrid RBF MLP-RNN classifier in two situations. With this classification method very good results are obtained for most subjects. Quite good results for all subjects are also obtained with the LDA-based normalized feature matrix. It is observed that for most subjects higher classification rates are obtained with the proposed method.  
In Figure 20, it is observed that the maximum classification rates of 95.6% (five subjects), are also obtained with the LDA classifier based on the ICA method. However, the most minimum classification rates (five subjects) are obtained with the same classification method. Stable classification methods, with which good results are obtained for all subjects, are the hybrid RBF MLP-RNN and LDA, which use features from multiresolution decomposition.  
Figure 19: Graphical representation of results with all classification methods used for subjects performing the task of motor imagery of the right hand.  
In conclusion, the safest, most accurate classifier with which to obtain the highest classification rates higher than those obtained with the traditional classifiers used in BCI, is the hybrid RBF MLP-RNN classifier proposed in the article. The highest rating rate obtained with this classifier is 95.67%. Although most of the maximum results of 94% are obtained with the LDA classifier based on the ICA method, the weakest results are also obtained with it. This classifier is very unstable and of very poor accuracy. Another classifier with which the lowest results are obtained, a proportion of approximately 30% of the total subjects, is the classifier that uses k-nearest neighbors algorithm (k-NN).  
Limitation and Future Works. The combined analysis of this study gave us a pool of subjects and a unique opportunity to study the learning effect across training sessions. However, there were only three training sessions for each subject. We did see some significant changes in behavioral performance such as the improvement in statistical analysis, and other trends of neurophysiological results including the increase in desynchronization/synchronization (ERD/ERS) lateralization for right-hand tasks, although they were not statistically significant. It is not clear yet when the change in behavioral performance might saturate and when significant changes in neurophysiological characteristics such as the \(r^{2}\) value, whether ERD lateralization might emerge and saturate in this study. In addition, the session interval for each participant varied between 1 day to 2 weeks due to scheduling conflicts for a large number of subjects. This might introduce an extra variance to the result. In the future, it is worth conducting a study that consists of more BCI sessions in a large number of subjects with more controllable session intervals. Furthermore, other metrics might help to explore the change in data distribution within and across the sessions, and this might extract some additional information that may help to optimize the experiment in the future.