Functional synergy recruitment index as a reliable biomarker of motor function and recovery in chronic stroke patients

Objective. Stroke affects the expression of muscle synergies underlying motor control, most notably in patients with poorer motor function. The majority of studies on muscle synergies have conventionally approached this analysis by assuming alterations in the inner structures of synergies after stroke. Although different synergy-based features based on this assumption have to some extent described pathological mechanisms in post-stroke neuromuscular control, a biomarker that reliably reflects motor function and recovery is still missing. Approach. Based on the theory of muscle synergies, we alternatively hypothesize that functional synergy structures are physically preserved and measure the temporal correlation between the recruitment profiles of healthy modules by paretic and healthy muscles, a feature hereafter reported as the FSRI. We measured clinical scores and extracted the muscle synergies of both ULs of 18 chronic stroke survivors from the electromyographic activity of 8 muscles during bilateral movements before and after 4 weeks of non-invasive BMI controlled robot therapy and physiotherapy. We computed the FSRI as well as features quantifying inter-limb structural differences and evaluated the correlation of these synergy-based measures with clinical scores. Main results. Correlation analysis revealed weak relationships between conventional features describing inter-limb synergy structural differences and motor function. In contrast, FSRI values during specific or combined movement data significantly correlated with UL motor function and recovery scores. Additionally, we observed that BMI-based training with contingent positive proprioceptive feedback led to improved FSRI values during the specific trained finger extension movement. Significance. We demonstrated that FSRI can be used as a reliable physiological biomarker of motor function and recovery in stroke, which can be targeted via BMI-based proprioceptive therapies and adjuvant physiotherapy to boost effective rehabilitation.This study was funded by the Fortüne-Program of the University of Tübingen (2452-0-0/2), the Bundesministerium für Bildung und Forschung (AMORSA (FKZ-16SV7754), REHOME (V5GR2001M1007-01)), EUROSTARS (SubliminalHomeRehab (FKZ: 01QE2023C E! 113928)) and the Basque Government Science Program (SINICTUS (2018222036), MODULA (KK-2019/00018), Elkartek-EXOTEK (KK-2016/00083)). N Irastorza-Landa's work was funded by the Basque Government's scholarship for predoctoral students

Control de mouse para computador mediante potenciales eléctricos oculares

El uso creciente de los computadores, y toda la tecnología asociada, ha promovido la creación de dispositivos de hardware cada vez más cómodos y eficientes para el usuario. Un número significativo de personas en situación de discapacidad no puede acceder a esta tecnología. Por esa razón, se han diseñado mecanismos, distintos a los tradicionales, para atender las necesidades de dichas personas. Entre estos mecanismos se han utilizado sistemas de control basados en biopotenciales. En este artículo, se presenta la construcción de un prototipo de mouse, cuyo movimiento en sentido horizontal es determinado por las señales eléctricas provenientes de desplazamientos oculares derecha-izquierda, que son captados por amplificadores de biopotenciales y posteriormente procesados y microcontrolados.The use of computers and associated technology, has promoted the development of more comfortable and efficient hardware devices. Many handicapped people can not access to this technology. As a result, innovative mechanisms have been designed to attend the requirements of these people. One of such mechanisms has been the control through biopotentials. This article presents the construction of a mouse prototype, which allows horizontal displacements in response to left and right ocular movements that are registered by instrumentation amplifiers, processed and microcontrolled

Experimental and statistical evaluation of a brain-computer interface (BCI) prototype.

En este trabajo se propone un estudio experimental y estadístico para comparar el prototipo de ICC con un sistema comercial (USBamp), estudiando si existen diferencias significativas entre los dos sistemas.In this paper, we propose an experimental and statistical design to compare the BCI prototype with a comercial device (USBamp), studyng if they show significant differences or not

Evaluación experimental y estadística de un prototipo de interfaz cerebro-computador (ICC)

Nowadays, brain-computer interfaces (BCI) are designed to be used in experimental and clinical studies, and their results allow the creation of new assistive technologies for people with motor disabilities. In 2008, a prototype of a BCI was developed in the School of Engineering of Antioquia and the University CES, which uses electroencephalography (EEG) to record the cognitive P300 evoked potential. In this paper, we propose an experimental and statistical design to compare this BCI prototype with a commercial device (USBamp), studying if they show significant differences or not. At first instance, this study is focused in some tests that characterize the systems, using as input deterministic signals with different values of frequency and amplitude, and which evaluation is made through mean square value, signals spectral density, response time and maximum peak during a stimulus. Secondly, we performed some analog tests in P300 signals evaluating signal energy and latency per channel. We used elements of statistical inference such as: the evaluation of a hypothesis for two means assuming unknown equal variances and equal means tests for two paired samples. According to the evidence, we concluded that the BCI prototype is suitable to measure and process EEG signals, but it is necessary to establish some improvement for certain treatments such as: the design of new circuits to optimize band width.En la actualidad, las Interfaces Cerebro-Computador (ICC) se diseñan con el fin de usarlas tanto en estudios experimentales como clínicos, y sus resultados permiten la creación de nuevas tecnologías asistidas para personas que se encuentran en situación de discapacidad motora. En el año 2008, se desarrolló un prototipo de una ICC en la Escuela de Ingeniería de Antioquia y la Universidad CES, que hace uso de la electroencefalografía (EEG) para detectar los potenciales evocados cognitivos P300. En este trabajo, se propone un estudio experimental y estadístico para comparar dicho prototipo de ICC con un sistema comercial (USBamp), estudiando si existen diferencias significativas entre los dos sistemas. El estudio se concentra en pruebas destinadas a la caracterización de sistemas, empleando como entrada, inicialmente, señales determinísticas con diferentes valores de frecuencia y amplitud, y cuya evaluación se hace a través del valor cuadrático medio, la densidad espectral de las señales, el tiempo de respuesta y el máximo pico ante un estímulo. En segunda instancia, se realizan pruebas análogas en señales de P300 evaluando la energía de la señal y el tiempo de latencia por canal. Se hace uso de elementos de inferencia estadística, como la evaluación de hipótesis para dos medias suponiendo varianzas desconocidas iguales y prueba de medias para dos muestras pareadas. De las pruebas evaluadas, se concluye que el prototipo de ICC es apto en cuanto la adquisición de EEG y su procesamiento, pero se establecen planes de mejoramiento para algunos tratamientos que incluyen el diseño de nuevos circuitos para mejorar el ancho de banda

Interfaz cerebro computadora (ICC) basada en el potencial relacionado con eventos P300: análisis del efecto de la dimensión de la matriz de estimulación sobre su desempeño

Una interfaz cerebro computadora (ICC) es un dispositivo que ayuda a personas con deficiencias motoras severas, al permitir la realización de una comunicación externa a partir de la actividad eléctrica del cerebro sin la asistencia de los nervios periféricos o de la actividad muscular, prometiendo además una mejora en la calidad de vida de los pacientes. En este proyecto se utilizó un sistema ICC basado en el paradigma P300, desarrollado en la Universidad Nacional de Entre Ríos. El sistema cuenta con un sistema no invasivo de adquisición de electroencefalograma, un amplificador Grass, el software BCI2000 y el paquete de simulación robótica Marilou. Adicionalmente, el sistema permite evaluar la aplicación de dicha ICC en el control de una silla de ruedas autopropulsada e inteligente. La presentación de estímulos para la generación del P300 se llevó a cabo con matrices de íconos que codifican las instrucciones de comandos o direcciones para la silla de ruedas. En el presente trabajo se probaron dos matrices con diferentes dimensiones y distribuciones, la primera de 4x5 y la segunda de 4x3. Se analizaron los porcentajes de clasificación que éstas arrojaron con el método de regresión SWLDA, donde se concluyó que la matriz de 4x3 presentaba mayores porcentajes de clasificación que la matriz 4x5. Las implicaciones con respecto al control de la silla se vislumbran como mayor confort y exactitud en el sistema inteligente.A brain computer interface BCI is a device that helps people with severs motor disabilities. It allows an external communication through the electrical activity of the brain without the assistance of the peripheral nerves or muscle activity. This project used a BCI system, based on P300 paradigm which was developed at Universidad Nacional de Entre Ríos. The system includes an EEG signal acquisition system that use external electrodes, a Grass amplifier, the BCI2000 software, and the Marilou robotic simulation tool. Additionally, the system allows the evaluation of the BCI application to control the movement of an intelligent and self-propelled wheelchair. The presentation of icons, which codified the instructions to command the wheelchair movements, was developed, in order to generate the stimulus for P300 generation. Two matrix with different size and distribution (4x5 and 4x3, row x column) were tested. We analyzed the percentage of classification obtained after the application of the regression method SWLDA, and we found that the major classification percentage was achieved with the 4x3 matrix. This study reveals that this process could be faster and more confortable for the user. And finally the subject decisions will have more correlation between the results of the system and his real desire

Decoding sensorimotor rhythms during robotic-assisted treadmill walking for brain computer interface (BCI) applications

Contains fulltext : 150695.pdf (publisher's version ) (Open Access)Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.22 p