An adaptive seamless assist-as-needed control scheme for lower extremity rehabilitation robots

Most control methods deployed in lower extremity rehabilitation robots cannot automatically adjust to different gait cycle stages and different rehabilitation training modes for different impairment subjects. This article presents a continuous seamless assist-as-needed control method based on sliding mode adaptive control. A forgetting factor is introduced, and a small trajectory deviation from reference normal gait trajectory is used to learn the rehabilitation level of a human subject in real time. The assistance torque needed to complete the reference normal gait trajectory is learned through radial basis function neural networks, so that the rehabilitation robot can adaptively provide the assistance torque according to subject’s needs. The performance and efficiency of this adaptive seamless assist-as-needed control scheme are tested and validated by 12 volunteers on a rehabilitation robot prototype. The results show that the proposed control scheme could adaptively reduce the robotic assistance according to subject’s rehabilitation level, and the robotic assistance torque depends on the forgetting factor and the active participation level of subjects

Development of Novel Compound Controllers to Reduce Chattering of Sliding Mode Control

The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and (iii) a 2 DOF robot manipulator. We proposed three sliding mode control methods such as robust sliding mode control (RSMC), new sliding mode control (NSMC), and fractional sliding mode control (FSMC). These controllers were applied on MEMS gyroscope, Exoskeleton robot, and robot manipulator. The performance of the three proposed sliding mode controllers was compared with conventional sliding mode control (CSMC). The simulation results verified that FSMC exhibits better performance in chattering reduction, faster convergence, finite-time convergence, robustness, and trajectory tracking compared to RSMC, CSMC, and NSFC. Also, the tracking performance of NSMC was compared with CSMC experimentally, which demonstrated better performance of the NSMC controller

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

Active interaction control applied to a lower limb rehabilitation robot by using EMG recognition and impedance model

Purpose – The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training. Design/methodology/approach – An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions. Findings – Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode. Originality/value – Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles

Controller design of a robotic orthosis using sinusoidal-input describing function model

Stroke is one of top leading causes of death in the world and it happens to more than 15 million people yearly. According to the National Stroke Association of Malaysia (NASAM), stroke is the third leading cause of death in Malaysia with around 40,000 cases reported annually. Forty percent of stroke survivors suffer from movement impairments after stroke. My grandfather was one of the victims and he was unable to attend any rehabilitation sessions due to several reasons. Hence, he lost the golden time to regain his movement and freedom. There are a lot of similar cases that happen daily in Malaysia. Besides, as the number of stroke patients increases yearly, the need for physiotherapists or rehabilitation machines equally increases. Hence, a low-cost clinical rehabilitation device is essential to provide assistance for an effective rehabilitation program and substitute the conventional method, as well as to reduce the burden of physiotherapists. In future, the proposed rehabilitation device would benefit not only stroke patients, but any patients who lost their normal walking ability including post-accident patients or those who suffer from spinal cord injury. The rehabilitation device aims to provide training assistance to patients not only in rehabilitation centres but also at home for daily training. The robotic orthosis is planned to be configured based on moving joint angles of human lower extremities. In the first stage of this research, angle-time characteristics for knee and hip swinging motion are utilised as a sagittal motion reference for the rehabilitation devices. The aim of following a proper gait cycle during rehabilitation training is to train patients to perform standing and swinging phases at proper timing and simultaneously provide the correct position reference to the patient during rehabilitation training. This can prevent patients from walking abnormally with an asymmetric gait cycle along or after the rehabilitation program. Besides, various limitations and the bulky structure of other rehabilitation devices lead to the design of the two-link lower limb rehabilitation device. This project aims to develop an assistive robotic rehabilitation device that generates a human gait trajectory for hemiplegic stroke patient gait rehabilitation in future. The shortcomings of other control applications due to environmental conditions and disturbances lead to the implementation of the describing function approach in the development of the devices. A sinusoidal-input describing function (SIDF) approach was implemented to linearize the nonlinear robotic orthosis with linear transfer function. The reason for utilising the SIDF approach is due to the nonlinear actual plant model with the present of load torque disturbances, discontinuous nonlinearities such as saturation and backlash, and also multivariable in the system. The nonlinear properties of the plant were proven in the preliminary stage of the research. A conventional controller, PID control combined with position and trajectory inputs were also applied to the system in the early stage of research. However, the experimental results were not satisfying. Finally, the SIDF approach was chosen to linearize the nonlinear system. Hence, generating a controller is much easier with a linear model of the nonlinear system. A SIDF approach was implemented to generate a controller for the multivariable, nonlinear closed loop system. Firstly, the SIDF approach enables the determination of the linear function of the nonlinear model known as the SIDF model. By utilising the linear model to mimic the behaviour of the nonlinear rehabilitation system, the controller for the nonlinear plant was able to be generated. In this research a controller based on linear control theory technique was used. The MATLAB library was used to design the lead-lag controller for the rehabilitation device. Various simulations such as step responses, tracking and decoupling of both links were performed on the generated controller with the nonlinear model to study the capability of the controller. Besides that, real life experiment testing was carried out to validate the feasibility of the controller designed via the SIDF approach. Simulation and experimental results were obtained, compared, and discussed. The highly accurate responses gained from experimental setup showed the robustness of the controller generated via SIDF approach. The implementation of the SIDF approach in a rehabilitation device (vertical two-link manipulator) is a first and hence, fulfils a novelty requirement for this research

Controller design of a robotic orthosis using sinusoidal-input describing function model

Stroke is one of top leading causes of death in the world and it happens to more than 15 million people yearly. According to the National Stroke Association of Malaysia (NASAM), stroke is the third leading cause of death in Malaysia with around 40,000 cases reported annually. Forty percent of stroke survivors suffer from movement impairments after stroke. My grandfather was one of the victims and he was unable to attend any rehabilitation sessions due to several reasons. Hence, he lost the golden time to regain his movement and freedom. There are a lot of similar cases that happen daily in Malaysia. Besides, as the number of stroke patients increases yearly, the need for physiotherapists or rehabilitation machines equally increases. Hence, a low-cost clinical rehabilitation device is essential to provide assistance for an effective rehabilitation program and substitute the conventional method, as well as to reduce the burden of physiotherapists. In future, the proposed rehabilitation device would benefit not only stroke patients, but any patients who lost their normal walking ability including post-accident patients or those who suffer from spinal cord injury. The rehabilitation device aims to provide training assistance to patients not only in rehabilitation centres but also at home for daily training. The robotic orthosis is planned to be configured based on moving joint angles of human lower extremities. In the first stage of this research, angle-time characteristics for knee and hip swinging motion are utilised as a sagittal motion reference for the rehabilitation devices. The aim of following a proper gait cycle during rehabilitation training is to train patients to perform standing and swinging phases at proper timing and simultaneously provide the correct position reference to the patient during rehabilitation training. This can prevent patients from walking abnormally with an asymmetric gait cycle along or after the rehabilitation program. Besides, various limitations and the bulky structure of other rehabilitation devices lead to the design of the two-link lower limb rehabilitation device. This project aims to develop an assistive robotic rehabilitation device that generates a human gait trajectory for hemiplegic stroke patient gait rehabilitation in future. The shortcomings of other control applications due to environmental conditions and disturbances lead to the implementation of the describing function approach in the development of the devices. A sinusoidal-input describing function (SIDF) approach was implemented to linearize the nonlinear robotic orthosis with linear transfer function. The reason for utilising the SIDF approach is due to the nonlinear actual plant model with the present of load torque disturbances, discontinuous nonlinearities such as saturation and backlash, and also multivariable in the system. The nonlinear properties of the plant were proven in the preliminary stage of the research. A conventional controller, PID control combined with position and trajectory inputs were also applied to the system in the early stage of research. However, the experimental results were not satisfying. Finally, the SIDF approach was chosen to linearize the nonlinear system. Hence, generating a controller is much easier with a linear model of the nonlinear system. A SIDF approach was implemented to generate a controller for the multivariable, nonlinear closed loop system. Firstly, the SIDF approach enables the determination of the linear function of the nonlinear model known as the SIDF model. By utilising the linear model to mimic the behaviour of the nonlinear rehabilitation system, the controller for the nonlinear plant was able to be generated. In this research a controller based on linear control theory technique was used. The MATLAB library was used to design the lead-lag controller for the rehabilitation device. Various simulations such as step responses, tracking and decoupling of both links were performed on the generated controller with the nonlinear model to study the capability of the controller. Besides that, real life experiment testing was carried out to validate the feasibility of the controller designed via the SIDF approach. Simulation and experimental results were obtained, compared, and discussed. The highly accurate responses gained from experimental setup showed the robustness of the controller generated via SIDF approach. The implementation of the SIDF approach in a rehabilitation device (vertical two-link manipulator) is a first and hence, fulfils a novelty requirement for this research

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

The potential of error-related potentials. Analysis and decoding for control, neuro-rehabilitation and motor substitution

Las interfaces cerebro-máquina (BMIs, por sus siglas en inglés) permiten la decodificación de patrones de activación neuronal del cerebro de los usuarios para proporcionar a personas con movilidad severamente limitada, ya sea debido a un accidente o a una enfermedad neurodegenerativa, una forma de establecer una conexión directa entre su cerebro y un dispositivo. En este sentido, las BMIs basadas en técnicas no invasivas, como el electroencefalograma (EEG) han ofrecido a estos usuarios nuevas oportunidades para recuperar el control sobre las actividades de su vida diaria que de otro modo no podrían realizar, especialmente en las áreas de comunicación y control de su entorno.En los últimos años, la tecnología está avanzando a grandes pasos y con ella la complejidad de dispositivos ha incrementado significativamente, ampliando el número de posibilidades para controlar sofisticados dispositivos robóticos, prótesis con numerosos grados de libertad o incluso para la aplicación de complejos patrones de estimulación eléctrica en las propias extremidades paralizadas de un usuario, que le permitan ejecutar movimientos precisos. Sin embargo, la cantidad de información que se puede transmitir entre el cerebro y estos dispositivos sigue siendo muy limitada, tanto por el número como por la velocidad a la que se pueden decodificar los comandos neuronales. Por lo tanto, depender únicamente de las señales neuronales no garantiza un control óptimo y preciso.Para poder sacar el máximo partido de estas tecnologías, el campo de las BMIs adoptó el conocido enfoque de “control-compartido". Esta estrategia de control pretende crear un sistema de cooperación entre el usuario y un dispositivo inteligente, liberando al usuario de las tareas más pesadas requeridas para ejecutar la tarea sin llegar a perder la sensación de estar en control. De esta manera, los usuarios solo necesitan centrar su atención en los comandos de alto nivel (por ejemplo, elegir un elemento específico que agarrar, o elegir el destino final donde moverse) mientras el agente inteligente resuelve problemas de bajo nivel (como planificación de trayectorias, esquivar obstáculos, etc.) que permitan realizar la tarea designada de la manera óptima.En particular, esta tesis gira en torno a una señal neuronal cognitiva de alto nivel originada como la falta de coincidencia entre las expectativas del usuario y las acciones reales ejecutadas por los dispositivos inteligentes. Estas señales, denominadas potenciales de error (ErrPs), se consideran una forma natural de intercomunicar nuestro cerebro con máquinas y, por lo tanto, los usuarios solo requieren monitorizar las acciones de un dispositivo y evaluar mentalmente si este último se comporta correctamente o no. Esto puede verse como una forma de supervisar el comportamiento del dispositivo, en el que la decodificación de estas evaluaciones mentales se utiliza para proporcionar a estos dispositivos retroalimentación directamente relacionada con la ejecución de una tarea determinada para que puedan aprender y adaptarse a las preferencias del usuario.Dado que la respuesta neuronal de ErrP está asociada a un evento exógeno (dispositivo que comete una acción errónea), la mayoría de los trabajos desarrollados han intentado distinguir si una acción es correcta o errónea mediante la explotación de eventos discretos en escenarios bien controlados. Esta tesis presenta el primer intento de cambiar hacia configuraciones asíncronas que se centran en tareas relacionadas con el aumento de las capacidades motoras, con el objetivo de desarrollar interfaces para usuarios con movilidad limitada. En este tipo de configuraciones, dos desafíos importantes son que los eventos correctos o erróneos no están claramente definidos y los usuarios tienen que evaluar continuamente la tarea ejecutada, mientras que la clasificación de las señales EEG debe realizarse de forma asíncrona. Como resultado, los decodificadores tienen que lidiar constantemente con la actividad EEG de fondo, que típicamente conduce a una gran cantidad de errores de detección de firmas de error. Para superar estos desafíos, esta tesis aborda dos líneas principales de trabajo.Primero, explora la neurofisiología de las señales neuronales evocadas asociadas con la percepción de errores durante el uso interactivo de un BMI en escenarios continuos y más realistas.Se realizaron dos estudios para encontrar características alternativas basadas en el dominio de la frecuencia como una forma de lidiar con la alta variabilidad de las señales del EEG. Resultados, revelaron que existe un patrón estable representado como oscilaciones "theta" que mejoran la generalización durante la clasificación. Además, se utilizaron técnicas de aprendizaje automático de última generación para aplicar el aprendizaje de transferencia para discriminar asincrónicamente los errores cuando se introdujeron de forma gradual y no se conoce presumiblemente el inicio que desencadena los ErrPs. Además, los análisis de neurofisiología arrojan algo de luz sobre los mecanismos cognitivos subyacentes que provocan ErrP durante las tareas continuas, lo que sugiere la existencia de modelos neuronales en nuestro cerebro que acumulan evidencia y solo toman una decisión al alcanzar un cierto umbral. En segundo lugar, esta tesis evalúa la implementación de estos potenciales relacionados con errores en tres aplicaciones orientadas al usuario. Estos estudios no solo exploran cómo maximizar el rendimiento de decodificación de las firmas ErrP, sino que también investigan los mecanismos neuronales subyacentes y cómo los diferentes factores afectan las señales provocadas.La primera aplicación de esta tesis presenta una nueva forma de guiar a un robot móvil que se mueve en un entorno continuo utilizando solo potenciales de error como retroalimentación que podrían usarse para el control directo de dispositivos de asistencia. Con este propósito, proponemos un algoritmo basado en el emparejamiento de políticas para el aprendizaje de refuerzo inverso para inferir el objetivo del usuario a partir de señales cerebrales.La segunda aplicación presentada en esta tesis contempla los primeros pasos hacia un BCI híbrido para ejecutar distintos tipos de agarre de objetos, con el objetivo de ayudar a las personas que han perdido la funcionalidad motora de su extremidad superior. Este BMI combina la decodificación del tipo de agarre a partir de señales de EEG obtenidas del espectro de baja frecuencia con los potenciales de error provocados como resultado de la monitorización de movimientos de agarre erróneos. Los resultados muestran que, en efecto los ErrP aparecen en combinaciones de señales motoras originadas a partir de movimientos de agarre consistentes en una única repetición. Además, la evaluación de los diferentes factores involucrados en el diseño de la interfaz híbrida (como la velocidad de los estímulos, el tipo de agarre o la tarea mental) muestra cómo dichos factores afectan la morfología del subsiguiente potencial de error evocado.La tercera aplicación investiga los correlatos neuronales y los procesos cognitivos subyacentes asociados con desajustes somatosensoriales producidos por perturbaciones inesperadas durante la estimulación eléctrica neuromuscular en el brazo de un usuario. Este estudio simula los posibles errores que ocurren durante la terapia de neuro-rehabilitación, en la que la activación simultánea de la estimulación aferente mientras los sujetos se concentran en la realización de una tarea motora es crucial para una recuperación óptima. Los resultados muestran que los errores pueden aumentar la atención del sujeto en la tarea y desencadenar mecanismos de aprendizaje que al mismo tiempo podrían promover la neuroplasticidad motora.En resumen, a lo largo de esta tesis, se han diseñado varios paradigmas experimentales para mejorar la comprensión de cómo se generan los potenciales relacionados con errores durante el uso interactivo de BMI en aplicaciones orientadas al usuario. Se han propuesto diferentes métodos para pasar de la configuración bloqueada en el tiempo a la asíncrona, tanto en términos de decodificación como de percepción de los eventos erróneos; y ha explorado tres aplicaciones relacionadas con el aumento de las capacidades motoras, en las cuales los ErrPs se pueden usar para el control de dispositivos, la sustitución de motores y la neuro-rehabilitación.Brain-machine interfaces (BMIs) allow the decoding of cortical activation patterns from the users brain to provide people with severely limited mobility, due to an accident or disease, a way to establish a direct connection between their brain and a device. In this sense, BMIs based in noninvasive recordings, such as the electroencephalogram (EEG) have o↵ered these users new opportunities to regain control over activities of their daily life that they could not perform otherwise, especially in the areas of communication and control of their environment. Over the past years and with the latest technological advancements, devices have significantly grown on complexity expanding the number of possibilities to control complex robotic devices, prosthesis with numerous degrees of freedom or even to apply compound patterns of electrical stimulation on the subjects own paralyzed extremities to execute precise movements. However, the band-with of communication between brain and devices is still very limited, both in terms of the number and the speed at which neural commands can be decoded, and thus solely relying on neural signals do not guarantee accurate control them. In order to benefit of these technologies, the field of BMIs adopted the well-known approach of shared-control. This strategy intends to create a cooperation system between the user and an intelligent device, liberating the user from the burdensome parts of the task without losing the feeling of being in control. Here, users only need to focus their attention on high-level commands (e.g. choose the final destination to reach, or a specific item to grab) while the intelligent agent resolve low-level problems (e.g. trajectory planning, obstacle avoidance, etc) to perform the designated task in the optimal way. In particular, this thesis revolves around a high-level cognitive neural signal originated as the mismatch between the expectations of the user and the actual actions executed by the intelligent devices. These signals, denoted as error-related potentials (ErrPs), are thought as a natural way to intercommunicate our brain with machines and thus users only require to monitor the actions of a device and mentally assess whether the latter is behaving correctly or not. This can be seen as a way to supervise the device’s behavior, in which the decoding of these mental assessments is used to provide these devices with feedback directly related with the performance of a given task so they can learn and adapt to the user’s preferences. Since the ErrP’s neural response is associated to an exogenous event (device committing an erroneous action), most of the developed works have attempted to distinguish whether an action is correct or erroneous by exploiting discrete events under well-controlled scenarios. This thesis presents the first attempt to shift towards asynchronous settings that focus on tasks related with the augmentation of motor capabilities, with the objective of developing interfaces for users with limited mobility. In this type of setups, two important challenges are that correct or erroneous events are not clearly defined and users have to continuously evaluate the executed task, while classification of EEG signals has to be performed asynchronously. As a result, the decoders have to constantly deal with background EEG activity, which typically leads to a large number of missdetection of error signatures. To overcome these challenges, this thesis addresses two main lines of work. First, it explores the neurophysiology of the evoked neural signatures associated with the perception of errors during the interactive use of a BMI in continuous and more realistic scenarios. Two studies were performed to find alternative features based on the frequency domain as a way of dealing with the high variability of EEG signals. Results, revealed that there exists a stable pattern represented as theta oscillations that enhance generalization during classification. Also, state-of-the-art machine learning techniques were used to apply transfer learning to asynchronously discriminate errors when they were introduced in a gradual fashion and the onset that triggers the ErrPs is not presumably known. Furthermore, neurophsysiology analyses shed some light about the underlying cognitive mechanisms that elicit ErrP during continuous tasks, suggesting the existence of neural models in our brain that accumulate evidence and only take a decision upon reaching a certain threshold. Secondly, this thesis evaluates the implementation of these error-related potentials in three user-oriented applications. These studies not only explore how to maximize the decoding performance of ErrP signatures but also investigate the underlying neural mechanisms and how di↵erent factors a↵ect the elicited signals. The first application of this thesis presents a new way to guide a mobile robot moving in a continuous environment using only error potentials as feedback which could be used for the direct control of assistive devices. With this purpose, we propose an algorithm based on policy matching for inverse reinforcement learning to infer the user goal from brain signals. The second application presented in this thesis contemplates the first steps towards a hybrid BMI for grasping oriented to assist people who have lost motor functionality of their upper-limb. This BMI combines the decoding of the type of grasp from low-frequency EEG signals with error-related potentials elicited as the result of monitoring an erroneous grasping. The results show that ErrPs are elicited in combination of motor signatures from the low-frequency spectrum originated from single repetition grasping tasks and evaluates how di↵erent design factors (such as the speed of the stimuli, type of grasp or mental task) impact the morphology of the subsequent evoked ErrP. The third application investigates the neural correlates and the underlying cognitive processes associated with somatosensory mismatches produced by unexpected disturbances during neuromsucular electrical stimulation on a user’s arm. This study simulates possible errors that occur during neurorehabilitation therapy, in which the simultaneous activation of a↵erent stimulation while the subjects are concentrated in performing a motor task is crucial for optimal recovery. The results showed that errors may increase subject’s attention on the task and trigger learning mechanisms that at the same time could promote motor neuroplasticity. In summary, throughout this thesis, several experimental paradigms have been designed to improve the understanding of how error-related potentials are generated during the interactive use of BMIs in user-oriented applications. Di↵erent methods have been proposed to shift from time-locked to asynchronous settings, both in terms of decoding and perception of the erroneous events; and it has explored three applications related with the augmentation of motor capabilities, in which ErrPs can be used for control of devices, motor substitution and neurorehabilitation.<br /