## 5 Discussion  
Many of the classification methods surveyed in reviews are evaluated using offline BCI data [13; 15; 27; 30; 31; 32; 37; 94]. However, an actual BCI application is fundamentally online. Based on the papers surveyed in this manuscript, we attempted to identify some guidelines on whether to use various types of classification and feature-extraction methods, neurofeedback modalities and BCI experimental parameters.  
One of the major issues in MI-BCI research is to define the direction of future studies regarding the classification methods. Since the BCI pipeline is evolving rapidly and novel approaches such as deep learning and transfer learning are increasingly used for discriminating EEG signals, one of our main concerns was if traditional and new approaches exhibit significant differences. According to our research, machine-learning methods are not inferior to deep-learning techniques. If we consider deep learning as an approach which is able to identify individualized separation rules for each subject, it would be logical to focus future work towards CNN and recurrent neural networks (RNN). Nonetheless, the application of transfer learning in future studies seems more appealing, since traditional and novel classifiers perform with similar accuracy. In addition, patients and healthy subjects did not show significant differences with respect to the classification performance; therefore, the training of robust and powerful traditional classification methods in the healthy domain and the adaptation to each patient's domain might be the next direction that should be taken in online BCI systems.  
Contrary to our results, [PERSON] et al., 2019 [81] achieved better classification performance with deep-learning models compared to traditional machine-learning techniques,  
Figure 3: Effect of BCI system parameters on the classification-accuracy relation between (**a**) the number of electrodes, and (**b**) the number of total trials used for the BCI training. The color of each point indicates if the data originates from healthy subjects (blue) or patients (red).  
suggesting a route ahead for developing new robust techniques for EEG signal decoding. [PERSON] et al., 2019 [81] was one of the few papers that attempted several classification methods with online data; however, in our research, only the algorithm with the best performance from each paper is mentioned. Another important detail of the classification accuracy reported in our manuscript is that, in many cases where the BCI performance was presented along with the neurorehabilitation period, we decided to outline the accuracy of the final week's sessions, since it was the value that the authors presented.  
Apart from the significant variety of factors influencing the classification performance, mentioned in the Section 4, we should not neglect the diverse methods of computing the classification performance in different studies. The usual way of calculating the classification performance in BCI systems is by training the algorithm with data deriving from several starting sessions and evaluating the performance based on the testing sessions. However, [PERSON] et al., 2017 [78], for instance, trained the classifier from data recorded during the first four runs of each session and testing the accuracy of the model in the last two runs of each session.  
Future surveys should include the clinical improvement in the patients and translate the impact of the classification accuracy to the clinical outcome. Unfortunately, this was impossible in this current version, since only three papers in our survey reported the clinical evaluation of the patients [19,78,80].