# Detecting the Speed Change Intention from EEG Signals:
## 1 Introduction  
Motor disabilities due to stroke impairment or spinal cord injury are an increasing problem in our modern society [1]. In the rehabilitation field, the current conventional motor therapies consist of the execution of intensive and repetitive sessions to mobilize the affected limb. These therapies are used to facilitate the recovery and learning postural movement allowing to adapt the disability to the daily life. However, these procedures are turning the focus on increasing cognitive involvement in order to increase the activation of neural plasticity processes [2]. Cognitive involvement can be achieved in many ways, including through the recording of brain activity establishing a direct connection between the mental task and the user. There are many ways to decode user's intention from brain activity [3]. For the detection of motor intention, the most common paradigms are: Movement-Related Cortical Potentials (MRCPs) and Event-Related (De)Synchronization (ERD/ERS), each with different latency and frequency characteristics regarding the eliciting event. MRCP are associated with low frequency changes (0.05-3 Hz) occurring as early as 0.5-2 s. ERD/ERS is described as a change of power in the alpha (8-12 Hz) and beta (13-30 Hz) bands. This event istemporally characterized up to 2 s before the self-paced movement [4]. Both are associated with movement imagination and execution. Concerning the practically decodification of both potentials, MRCP provides timing information about movement stages and planning execution, but it is very unstable against noise and not so useful in single trial analysis [5]. On the other hand, ERD/ERS does not provide precise timing information about different stages of movement planning, preparation and execution, but ERD/ERS has been shown to be detectable from single trial EEG [6] and less variable to noise than MRCP [7]. These qualities of the ERD/ERS potential allow prediction of motor intention and could be useful for assisting the user's movement. This connection between this decoding potential recorded in the scalp and the motor execution is called Brain-Machine Interface (BMI). The information is registered by noninvasive electroencephalography (EEG) and the interpretation of the patterns extracted from it can be used to send commands to an assistive device. This can be used to control devices such as a weelchair or an exoskeleton and help non-able bodied subjects not only from rehabilitation, but assistance perspectives. Both approaches have been studied in the literature: the rehabilitation approach is focused in the BMI neuroplasticity elicitation [8]; and the assistance, more focused on the control loop decision efficiency [9]. The ERD/ERS intention paradigm can facilitate the device adaptation in different gait scenarios. Since most of the neural changes appear before the onset of movement, the assistive movement appears more natural and eliminates undesirable delays and frustrating waits. The possibility of using BMIs for movement assistance has led to the study of EEG-encoded movement patterns. There are several studies analyzing different lower-limb motion controls, for example: start-stop, change direction, stand-up/sit-down and change speed. The decoding of start-stop walking is analyzed in [PERSON] et al. in offline and pseudo-online analysis. In the study, the data window was chosen both in an interval of \(-2\) to 2 s after it. The Stockwell transform and the empirical mode decomposition (EMD) [10] were used to extract time-frequency features. In [PERSON] et al. [11], start walking EEG was collected and analyzed in pseudo-online methology using a window from \(-1.5\) to 0 s before the movement, performing an unbalancing clas training. The temporal MRCP and spectral ERD/ERS characteristics were tested separately and in combination, with a predominance of the influence precision of the MRCP features. In [PERSON] et al.