# Detecting the Speed Change Intention from EEG Signals:
## 4 Discussion
### Study Limitations  
The manuscript presents a novel study for the prediction of the intention to change speed by EEG analysis. The final objective is to assess the viability of a real control of assistive mobility devices. Nevertheless, it is important to remark that this is a preliminary study with some limitations.  
The database is formed by five subjects, which, although it may be short, it is kept within the ranges of other studies. Even though, the results of the offline model are acceptable, the results of the pseudo-online methodology are not equivalent and completely consistent. New window segmentation methods could be better suited to characterize the classification model used in the pseudo-online test.  
* Try a moving window, to better represent the Walk class.
* Subdivide in smaller windows, to have a window that better represents the classes, at the cost of introducing more variance in the analysis window, since the ERD/ERS pattern could vary throughout the subdivided windows.
* Test a representation of the unbalanced walk class.
* Test another continuous decoding classifiers, such as GMM or a decomposition of components with some threshold limits. Decomposition methods like EMD could not be valid for real-time processing, but eigendecomposition [30] or cwt decomposition components could.  
An important point in the development of a real-time experiment is that once the training is done, the model must be created in an acceptable time. This becomes even more important when this is validated on patients. The creation of a pre-selection of features could speed up the process. These pre-selected features can be optimized and their combinations customized. In this preliminary study, a pre-selected group of features were chosen.  
Regarding the BMI efficacy, the number of false positives in this study is close to other studies in similar conditions. The exoskeleton closed-loop control experiment shown presents a concept study. From the experience of this pilot test, several consequences can be extracted. First, the reduced number of test repetitions was due to the fact that using the exoskeleton training methodology in passive mode results in a firing process. Decreasing the number of trials may be an option. Three protocol alternatives could be used to train the model:* In the first one, the model would be trained with the exoskeleton running in two speeds that would be changed when the exoskeleton gauges register a change. This needs, further research to be carried out.
* In the second one, the model would be trained with the exoskeleton running at two speeds and the user would be the one who would indicate the change by means of a button when they wants to change the speed. This could condition the EEG potential, due to the different states of consciousness.
* In the third, the model would be created without an exoskeleton and later the closed-loop would be made with an exoskeleton. This could reduce the subject's fatigue, but could generate training vectors too differenct with respecto to the testing ones.  
As the ultimate goal is to improve assistance, reducing FPs is a priority issue. To this end, more realistic models that represent better the data must be addressed. All of the above is important for this. In addition, new feature extractions should be explored, optimizing the filters that could improve the signal to noise ratio. There is not much exploration on the usefulness and differences between noise filters as \(H_{\infty}\) or ASR depending on what kind of potentials are decoded.