Falangismo y propaganda cultural en el "Nuevo Estado": La Revista Escorial (1940-1950)

Tesis Univ. Granada. Departamento de Literatura Española. Leída el 7 de mayo de 200

Low cost 3D-printed hand exoskeleton controlled by a BCI

[Abstract] The aim of this work was to build and design a low-cost active hand exoskeleton to be used in rehabilitation of post-stroke patients. The hand exoskeleton was 3D printer and it allowed an active flexion and extension of the fingers. The exoskeleton designed was powered by three servomotors which pulled tendons to shorten the distance over or under the joints. This produced a natural hand movement close to a biological one. The exoskeleton was controlled by a Brain-Machine interface (BMI) utilizing an EEG-cap for measuring the brain activity. This way, two subjects tested the whole system opening and closing the hand exoskeleton by using only their thoughts.[Resumen] El objetivo de este trabajo fue construir y diseñar un exoesqueleto de mano activa de bajo costo para ser utilizado en la rehabilitación de pacientes post-accidente cerebro vascular. El exoesqueleto de mano era una impresora 3D que permitía una flexión activa y extensión de los dedos. El exoesqueleto diseñado fue impulsado por tres servomotores que tiraban de los tendones para acortar la distancia sobre o debajo de las articulaciones. Esto produjo un movimiento natural de la mano cerca de uno biológico. El exoesqueleto fue controlado por una interfaz Brain-Machine (BMI) que utiliza una tapa EEG para medir la actividad cerebral. De esta manera, dos sujetos probaron todo el sistema abriendo y cerrando el exoesqueleto de la mano usando solo sus pensamientos

Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study

The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation using the Fourier analysis. This paper explores the decomposition of the brain rhythms based on the Empirical Mode Decomposition, as an alternative for the analysis of electroencephalographic (EEG) signals, due to its adaptive capability to the local oscillations of the data, showcasing it as a viable tool for future BMI algorithms based on motor related events.by the Spanish Ministry of Science and Innovation, the Spanish State Agency of Research, and the European Union through the European Regional Development Fund in the framework of the project Walk—Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (RTI2018-096677-B-I00);and by the Consellería de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana) and the European Social Fund in the framework of the project Desarrollo de nuevas interfaces cerebro-máquina para la rehabilitación de miembro inferior (GV/2019/009Authors would like to thank specially Kevin Nathan and the rest of the laboratory of JC-V for their help during the experimental trials and Atilla Kilicarslan for his help with the implementation of H∞ algorithm

Análisis de la fatiga muscular en el biceps mediante una arquitectura de bajo coste basada en Arduino-eHealth

[Resumen] En este trabajo se ha realizado un análisis de la fatiga muscular mediante una arquitectura de bajo coste. Ésta está basada en Arduino y eHealth. Uno de los objetivos principales ha sido el de realizar todo el procesamiento en tiempo real dentro de la propia arquitectura utilizando Matlab únicamente como medio de visualización y envío de ordenes simples. Para analizar las señales musculares se ha realizado un ajuste exponencial para determinar la fatiga obteniendo resultados satisfactorios.https://doi.org/10.17979/spudc.978849749808

Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton

Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity through repetitive motor actions and cognitive engagement of the subject. Therefore, the cognitive implication of the user is a key aspect in BMI applications, and it is important to assure that the mental task correlates with the actual motor action. However, the process of walking is usually an autonomous mental task that requires a minimal conscious effort. Consequently, a brain-machine interface focused on the attention to gait could facilitate sensory integration in individuals with neurological impairment through the analysis of voluntary gait will and its repetitive use. This way the combined use of BMI+exoskeleton turns from assistance to restoration. This paper presents a new brain-machine interface based on the decoding of gamma band activity and attention level during motor imagery mental tasks. This work also shows a case study tested in able-bodied subjects prior to a future clinical study, demonstrating that a BMI based on gamma band and attention-level paradigm allows real-time closed-loop control of a Rex exoskeleton.This research was funded by the Spanish Ministry of Science and Innovation through grant CAS18/00048 José CastillejoBy the Spanish Ministry of Science and Innovation, the Spanish State Agency of Research, and the European Union through the European Regional Development Fund in the framework of the project Walk–Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (RTI2018-096677-B-I00);by theConsellería de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana),the European Social Fund in the framework of the project Desarrollo de nuevas interfaces cerebro-máquina para la rehabilitación de miembro inferior (GV/2019/009).Authors would like to thank especially Kevin Nathan and the rest of the laboratory of JC-V for their help during the experimental trials, and Atilla Kilicarslan for his help with the implementation of H1 algorith

Study of the Functional Brain Connectivity and Lower-Limb Motor Imagery Performance After Transcranial Direct Current Stimulation

The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain–computer interface (BCI) based on electroencephalography is simulated in offline analysis to study the effect that tDCS has over different electrode configurations for the BCI. This way, the BCI performance is used as a validation index of the effect of the tDCS setup by the analysis of the classifier accuracy of the experimental sessions. In addition, the relationship between the brain connectivity and the BCI accuracy performance is analyzed. Results indicate that tDCS group, in comparison to the placebo sham group, shows a higher significant number of connectivity interactions in the motor electrodes during MI tasks and an increasing BCI accuracy over the days. However, the asymmetric tDCS setup does not improve the BCI performance of the electrodes in the intended hemisphereThis research has been carried out in the framework of the project Walk — Controlling lower-limb exoskeletons by means of BMIs to assist people with walking disabilities (RTI2018-096677-B-I00Funded by the Spanish Ministry of Science and Innovation, the Spanish State Agency of Research and the European Union through the European Regional Development Fund;by the Consellería de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana) and the European Social Fund in the framework of the project ‘Desarrollo de nuevas interfaces cerebro-m´aquina para la rehabilitaci`on de miembro inferior’ (GV/2019/009).Also, the Mexican Council of Science and Technology (CONACyT) provided J. A. Gaxiola-Tirado his scholarshi

Improving Real-Time Lower Limb Motor Imagery Detection Using tDCS and an Exoskeleton

The aim of this work was to test if a novel transcranial direct current stimulation (tDCS) montage boosts the accuracy of lower limb motor imagery (MI) detection by using a real-time brain-machine interface (BMI) based on electroencephalographic (EEG) signals. The tDCS montage designed was composed of two anodes and one cathode: one anode over the right cerebrocerebellum, the other over the motor cortex in Cz, and the cathode over FC2 (using the International 10–10 system). The BMI was designed to detect two MI states: relax and gait MI; and was based on finding the power at the frequency which attained the maximum power difference between the two mental states at each selected EEG electrode. Two different single-blind experiments were conducted, E1 and a pilot test E2. E1 was based on visual cues and feedback and E2 was based on auditory cues and a lower limb exoskeleton as feedback. Twelve subjects participated in E1, while four did so in E2. For both experiments, subjects were separated into two equally-sized groups: sham and active tDCS. The active tDCS group achieved 12.6 and 8.2% higher detection accuracy than the sham group in E1 and E2, respectively, reaching 65 and 81.6% mean detection accuracy in each experiment. The limited results suggest that the exoskeleton (E2) enhanced the detection of the MI tasks with respect to the visual feedback (E1), increasing the accuracy obtained in 16.7 and 21.2% for the active tDCS and sham groups, respectively. Thus, the small pilot study E2 indicates that using an exoskeleton in real-time has the potential of improving the rehabilitation process of cerebrovascular accident (CVA) patients, but larger studies are needed in order to further confirm this claim

Quantification of Extent of Muscle-skin Shifting by Traversal sEMG Analysis Using High-density sEMG Sensor

Averaging electromyographic activity prior to muscle synergy computation is a common method employed to compensate for the inter-repetition variability usually associated with this kind of physiological recording. Capturing muscle synergies requires the preservation of accurate temporal and spatial information for muscle activity. The natural variation in electromyography data across consecutive repetitions of the same task raises several related challenges that make averaging a non-trivial process. Duration and triggering times of muscle activity generally vary across different repetitions of the same task. Therefore, it is necessary to define a robust methodology to segment and average muscle activity that deals with these issues. Emerging from this need, the present work proposes a standard protocol for segmenting and averaging muscle activations from periodic motions in a way that accurately preserves the temporal and spatial information contained in the original data and enables the isolation of a single averaged motion period. This protocol has been validated with muscle activity data recorded from 15 participants performing elbow flexion/extension motions, a series of actions driven by well-established muscle synergies. Using the averaged data, muscle synergies were computed, permitting their behavior to be compared with previous results related to the evaluated task. The comparison between the method proposed and a widely used methodology based on motion flags, shown the benefits of our system maintaining the consistency of muscle activation timings and synergie

Estudio preliminar de la detección de cambios de velocidad de la marcha a partir de señales EEG

[ES] El análisis de las señales cerebrales para la asistencia en pacientes con movilidad reducida es un área de investigación continua en la que las nuevas tecnologías ofrecen un amplio espectro de posibilidades para la ayuda activa como los exoesqueletos y las interfaces cerebro-maquinas (BMI). En este trabajo nos centramos en realizar una interfaz BMI para el control de velocidad del miembro inferior. El objetivo de este estudio es analizar las señales electroencefalográficas (EEG) para obtener un indicador que relacione la información cerebral con marcadores de velocidad en la marcha. Para el procesamiento de las señales EEG analizamos los ritmos sensoriomotores correspondientes a las bandas Alfa y Beta en la zona motora. La interfaz BMI debe discernir entre dos estados marcha e intención de cambiar de velocidad. La creación de dicho modelo requiere caracterizar el momento del cambio, para la fase de entrenamiento. Hemos utilizado el equipo de captura de movimiento Tech MCS V3 basado en sensores inerciales. Analizamos las componentes frecuenciales de la aceleración en el dominio temporal mediante la transformada continua Wavelet (CWT). En este trabajo realizamos un estudio menor para analizar la marcha y otro principal para la validación de la interfaz BMI planteada. Tres usuarios sanos participaron en el estudio. El protocolo tiene tres fases, el usuario espera parado unos segundos tras los cuales decide comenzar la marcha a un ritmo visiblemente lento y llegado un punto voluntariamente cambia de velocidad manteniéndola unos segundos. El experimento consta de 40 repeticiones. Debido a la gran generación de artefactos durante la marcha los componentes de la señal EEG fueron descompuestos mediante ICA y rechazados según su componente espectral y un criterio apropiado. Los resultados fueron procesados con varias configuraciones de electrodos de la zona motora, en diferentes bandas de frecuencia y con y sin eliminación de artefactos. El porcentaje máximo de acierto para los tres usuarios a la hora de distinguir entre las dos clases es del 58%, no siendo resultados lo suficientemente remarcables para poder validar de forma confiable la interfaz BMI planteada.[EN] The main objective of this work is analysing electroencephalographic (EEG) signals to get a mark to link brain information with characteristic indicators of the walk speed. This starts with developing a Brain-Machine Interface (BMI) and designing a validation protocol to allow to conclude an accuracy rate to assure reliable control of the BMI. It pretends to create a robust arquitecture that will be improvable in the future being able to provide a feedback with the pacient in real time.Esta investigación ha sido realizada en el marco del proyecto Walk - Control de exoesqueletos de miembro inferior mediante interfaces cerebro-máquina para asistir a personas con problemas de marcha (RTI2018-096677-B-I00), financiado por el Ministerio de Ciencia, Innovación y Universidades (MCIU), la Agencia Estatal de Investigación (AEI) y la Unión Europea a través del Fondo Europeo de Desarrollo Regional (FEDER).Quiles Zamora, V.; Iáñez, E.; Ortiz, M.; Azorín, J. (2019). Estudio preliminar de la detección de cambios de velocidad de la marcha a partir de señales EEG. En 11º Simposio CEA de Bioingeniería. Editorial Universitat Politècnica de València. 97-110. https://doi.org/10.4995/CEABioIng.2019.10034OCS9711

Evaluación de artefactos en un Sistem BMI para la medición de niveles de atención durante movimiento con exoesqueleto de miembro inferior

[Resumen] Con la creciente preocupación social ante el número de personas que sufren de disfunciones motoras, surgen múltiples estudios orientados a la mejora de las terapias de rehabilitación. En este aspecto, ha surgido una rama científica centrada en el desarrollo de interfaces cerebro-máquina que controlan el movimiento de un exoesqueleto durante terapias de rehabilitación. En este trabajo se presenta una interfaz cerebro-máquina capaz de proporcionar a un exoesqueleto el nivel de atención que un paciente presenta durante la marcha. Para evaluar la validez de los resultados de este sistema, se realizan pruebas empleando y no empleando un exoesqueleto. Los resultados muestran una caída considerable en el los aciertos cuando se utiliza el exoesqueleto debido a la aparición de artefactos de movimiento