# Detecting the Speed Change Intention from EEG Signals:
## 4 Discussion  
In this section, the peculiarities of previous works have been reviewed, in order to analyze which are the factors that differentiate our work and what can be interpreted from our results. The decoding of movement intention through EEG has been analyzed in the bibliography attending to four criteria:  
* The time window of the EEG information used. The intention has been analyzed only with data signal before the point labeled as beginning of the movement [6,11,12,13,14,15] or also data signal during and after it [10,17].
* Analysis of the cerebral activity to study the paradigm in asynchronous (offline, pseudo-online) or synchronous (online) processing [13,15]. Asynchronous processing studies simulate the control of the rehabilitation/assistive device in open-loop while synchronous BMIs can control it in open or closed-loop.
* Most EEG-based intent studies show an analysis of brain activity without taking into account the implementation part of the BMI, which requires a closed-loop analysis with visual or auditory feedback [27] or sensitive motor feedback provided by an assistive control devices [13,15].
* Level of planning in which the movement was attempted: some studies establish an acoustic cue or mark when the subjects should perform the intention [15,16,28], other determine a planning period based in visual cues in which the users take the decision to perform the action [11,17], while other perform the action by their own will within a region limitation, like in this study [14], or with less conditions [10,12,29].  
Figure 6: Repetitions 1, 2, 3 and 4 activation schema for the subject 4 at the E2 in the closed-loop test. Repetitions 5 and 6 were similar to 1 and 2 repetitions. At the top of the image the order of the tasks in color code as specified in Figure 2b). At the bottom of the image the activation pattern of the exoskeleton. In blue the speed 1 stage where the BMI cannot send commands. In green the correct speed phase (TP) and in red if it should not be activated as established (FP).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Repetition 1** & **Repetition 2** & **Repetition 3** & **Repetition 4** & **Repetition 5** & **Repetition 6** \\ \hline FP-test, walk Speed 1 & 0.2 s & 3.4 s & - & - & 3.2 s & 1.4 s \\ Wainting user Intention & - & - & 2.8 s & 3.8 s & - & - \\ \hline \end{tabular}
\end{table}
Table 5: Differences in seconds between FP-test task start and false positive activation and between waiting user intention task start and activation of speed command 2 for the FP and TP cases.  
There are not many studies that analyze the intention in a task that switches from motion to motion, and those that analyze in the MRCP band can be greatly compromised by low-frequency artifacts. In this study, it has been reported the feasibility of decoding the user intention at bands combinations 8-14 Hz, 15-22 Hz and 23-30 Hz in speed changes for either Increase, Decrease or Both. The closed-loop control is not yet reliable enough to create an interface capable of assisting or designing rehabilitation therapies focused on patients. To address this problem, this work has proposed an interface with protocols capable of operating in real time and producing decision making according to the user's intention in some of the cases. Some of the key aspects of this work will be discussed below.