# Detecting the Speed Change Intention from EEG Signals:
## 1 Introduction
[6], the temporal analysis interval was chosen between \(-2\) s to 0 s before the movement onset, as an special subdivided windows' feature extraction method. All Hjorth parameters were computed and a pseudo-online analysis was performed. In [12], a wavelet transform analysis was performed in the same procedural experiment. Both experiments [6; 12] work with the Artifact Subspace Reconstruction (ASR) filter and a baseline correction for the pseudo-online Linear Support Vector Machine (SVM) classification. However, the former one has been reported as a huge gap to bridge in real-time applications. In [13], another gait-stop intention paradigm was performed with ASR and combining common spatial pattern (CSP) and linear discriminant analysis classification. In [14], the change of direction is studied offline using a window prior to the change, a KNN classifier differentiates a large and varied number of features. In [15], a protocol for an exoskeleton control based on the decoding of the EEG signal is presented. It tries to differentiate between four motion classes (walk, turn-right, stop and turn-left). This is done by a pseudo-online analysis using a MRCP paradigm with ASR filtering and multiple kernel learning, reporting high accuracy rates. However, the protocol marks when the user should perform the action, not allowing a free intention of the subject. This makes the signals susceptible to possible Event Related Potentials (ERP). A similar protocol is performed at [16], with the same motor states analyzed, but the MRCP patterns are reduced with local fishers's discriminant and classified with an unsupervised Gaussian Mixture Model (GMM). Finally, the change of speed has also been analyzed. In [17], [PERSON] et al. study the correlation between the EEG and increment/decrement speed changes. In the offlineanalysis, the speed change window is chosen between \(-4\) and \(4\) s after the change, while the constant speed window is chosen between \(-12\) and \(-4\) s. reported suppression in mu rhythm ranging from \(-1\) to \(3\) s, nevertheless the largest magnitude of ERD/ERS is located in the mu band between \(0.5\) and \(2\) s and beta rhythm starting near the onset and ending at \(2\) s. The most studied approach of all these is the start and stop one. This action is very interesting for voluntary gait control. Regarding closed-loop control, the relevant investigations in the literature are:  
The aforementioned research in [13] proposes a protocol to decode start/stop intentions from EEG signals based on ERD/ERS features in a pseudo-online methodology with an exoskeleton. It reports a TP of \(89.0\pm 0.8\%\) and FP/min of \(13.6\pm 0.4\). closed-loop real-time detection results report a TP of \(92.3\%\) and a FP/min ratio of \(28.4\pm 35.2\). In addition, in the work of [PERSON] et al. [16] the closed-loop only is performed for the walk and stop patterns. Although the decoding results are close to \(100\%\), reliable metrics are not reported. However, the modulation of the transition between gait states, such as direction or speed changes are a less researched topic and there are not many examples in the literature. Training the model with the exoskeleton is a critical point in these paradigms. In [15; 16], marks on the ground are used to instruct the subject to perform the change at a certain point. The model must be trained with data acquired with the exoskeleton, so that later it can be tested in closed-loop control efficiently and with good performance [18]. No closed-loop studies were found for these paradigms. This indicates that there is a clear gap on this approach in the literature and that further research is needed for the development of robust closed-loop models. This work tries to close this gap developing a closed-loop experiment to detect the intention of changing speed moments before the subject has executed it. The objective is to improve the tool to a viable stage for its posterior use in non-able bodied subjects. With this in mind, several studies are performed.