Vicente Quiles

Las interfaces cerebro-computadora (BCIs) se han consolidado como una herramienta prometedora para la neurorrehabilitación. Entre los paradigmas de control más utilizados en este contexto se encuentra la imaginación motora, debido a su capacidad para activar áreas corticales relacionadas con el movimiento. No obstante, el entrenamiento previo del usuario y el coste asociado pueden dificultar su aplicación clínica. Con esto en mente, se ha desarrollado un sistema de bajo coste basado en un videojuego controlado por imaginación motora, diseñado para ser utilizado de forma autónoma en el hogar y así fomentar la participación activa en la rehabilitación. El sistema fue validado experimentalmente con dos sujetos de control: el primero alcanzó una exactitud media de 0.69~$\pm$0.04 y el segundo, de 0.80$\pm$~0.01. Ambos completaron con éxito las partidas, lo que sugiere que el uso del videojuego puede contribuir positivamente al proceso de rehabilitación, ofreciendo un apoyo accesible y motivador a las rutinas clínicas.

This article presents an exhaustive analysis of the works present in the literature pertaining to transcranial direct current stimulation(tDCS) applications. The aim of this work is to analyze the specific characteristics of lower-limb stimulation, identifying the strengths and weaknesses of these works and framing them with the current knowledge of tDCS. The ultimate goal of this work is to propose areas of improvement to create more effective stimulation therapies with less variability.

Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented.

Brain–Computer Interfaces (BCI) are systems that allow external devices to be controlled by means of brain activity. There are different such technologies, and electroencephalography (EEG) is an example. One of the most common EEG control methods is based on detecting changes in sensorimotor rhythms (SMRs) during motor imagery (MI). The aim of this study was to assess the laterality of cortical function when performing MI of the lower limb. Brain signals from five subjects were analyzed in two conditions, during exoskeleton-assisted gait and while static. Three different EEG electrode configurations were evaluated: covering both hemispheres, covering the non-dominant hemisphere and covering the dominant hemisphere. In addition, the evolution of performance and laterality with practice was assessed. Although sightly superior results were achieved with information from all electrodes, differences between electrode configurations were not statistically significant. Regarding the evolution during the experimental sessions, the performance of the BCI generally evolved positively the higher the experience was.