# Detecting the Speed Change Intention from EEG Signals:
## 5 Conclusions  
A brain-machine interface for the classification of EEG signals related to intention process and speed changes in real time has been proposed. The model for the class intention has been trained with only information from the moments before the change. First, data were analyzed in offline cross-validation and in a simulated real time analysis (pseudo-online). The best features were selected in a generic way to describe the intention paradigm. Second, a real-time proof of concept with the exoskeleton was designed. It allowed to control the increase of speed of an exoskeleton in real time, and to validate the feasibility of the previous study in a realistic way.  
The study has concluded that the Hjorth parameters are viable for decoding the intention of speed changes. Among the different mental tasks (increase, decrease or both), the intention of increase speed offered the best results in offline and pseudo-online analysis.  
The design of training protocols that allow the creation of a robust general model with sufficient repetitions and not generate excessive fatigue is a pending challenge. Furthermore, noise filtering is another important aspect for real-time applications. Our approach has applied filters, such as \(H^{\infty}\) and Laplacian filters to improve the signal to noise ratio and mitigate artifacts. Our state variable filter methodology allows us to analyze the signal in real time without distortion and border effects. In addition, these methods must be fast, in order to be suitable for real-time analysis.  
As was commented in the study limitations Section 4.4, future works will explore different methodologies to obtain a more robust classifier model that can be better adapted to real-time conditions. In addition, the closed-loop protocol will be improved, especially in the training steps to avoid the fatigue of the subject and limit the protocol times.  
Conceptualization, V.Q. and E.I.; methodology, V.Q., L.F. and E.I.; software, V.Q., L.F., J.M.C. and E.I.; validation, E.I. and M.O.; formal analysis, V.Q., L.F. and J.M.C.; investigation, V.Q. and L.F.; resources, M.O., E.I. and J.M.A.; data curation, V.Q., J.M.C. and E.I.; writing--original draft preparation, V.Q.; writing--review and editing, V.Q., M.O. and E.I.; visualization, V.Q.; supervision, E.I., M.O. and J.M.A.; project administration, J.M.A.; funding acquisition, J.M.A. All authors have read and agreed to the published version of the manuscript.  
This research is part of grant RTI2018-096677-B-100, funded by MCIN/AEI/10.13039/501100011033 and by \"ERDF A way of making Europe\"; by the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana); and by the Miguel Hernandez University of Elche through the grant \"Convocatoria de Ayudas a la Investigacion 2020\" in the framework of \"Pla de millora cientifica i transferencia del coniexement\" of the project \"Deteccion de eventos motores mediante IMUs para etiquetado de señales EEG (DETECTA)\".  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Miguel Hernandez University of Elche (DISJAP03.18 and 22 January 2019).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.