Brain–computer interfaces (BCI) can extract information about the subject’s intentions by registering and processing electroencephalographic (EEG) signals to generate actions on physical systems. Steady-state visual-evoked potentials (SSVEP) are produced when the subject stares at flashing visual stimuli. By means of spectral analysis and by measuring the signal-to-noise ratio (SNR) of its harmonic contents, the observed stimulus can be identified. Stimulus color matters, and some authors have proposed red because of its ability to capture attention, while others refuse it because it might induce epileptic seizures. Green has also been proposed and it is claimed that white may generate the best signals. Regarding frequency, middle frequencies are claimed to produce the best SNR, although high frequencies have not been thoroughly studied, and might be advantageous due to the lower spontaneous cerebral activity in this frequency band. Here, we show white, red, and green stimuli, at three frequencies: 5 (low), 12 (middle), and 30 (high) Hz to 42 subjects, and compare them in order to find which one can produce the best SNR. We aim to know if the response to white is as strong as the one to red, and also if the response to high frequency is as strong as the one triggered by lower frequencies. Attention has been measured with the Conner’s Continuous Performance Task version 2 (CPT-II) task, in order to search for a potential relationship between attentional capacity and the SNR previously obtained. An analysis of variance (ANOVA) shows the best SNR with the middle frequency, followed by the low, and finally the high one. White gives as good an SNR as red at 12 Hz and so does green at 5 Hz, with no differences at 30 Hz. These results suggest that middle frequencies are preferable and that using the red color can be avoided. Correlation analysis also show a correlation between attention and the SNR at low frequency, so suggesting that for the low frequencies, more attentional capacity leads to better results.

Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.

Motor imagery has been suggested as an efficient alternative to improve the rehabilitation process of affected limbs. In this study, a low-cost robotic guide is implemented so that linear position can be controlled via the user’s motor imagination of movement intention. The patient can use this device to move the arm attached to the guide according to their own intentions. The first objective of this study was to check the feasibility and safety of the designed robotic guide controlled via a motor imagery (MI)-based brain–computer interface (MI-BCI) in healthy individuals, with the ultimate aim to apply it to rehabilitation patients. The second objective was to determine which are the most convenient MI strategies to control the different assisted rehabilitation arm movements. The results of this study show a better performance when the BCI task is controlled with an action–action MI strategy versus an action–relaxation one. No statistically significant difference was found between the two action–action MI strategies.

In this paper, a bibliometric review is conducted on brain–computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence. This article allows for the identification of different bibliometric indicators such as the research process, evolution, visibility, volume, influence, impact, and production in the field of brain–computer interfaces for MI and SSVEP paradigms in rehabilitation and robotics applications from 2000 to August 2024.

The purpose of this communication is to contribute to the development of a new trajectory management capability for an engine-out transportation aircraft. Engine-out is a dramatic situation for flight safety and this study focuses on the design of a management system for emergency trajectories at this special situation. First the gliding characteristics and flying qualities of a transport aircraft with total engine failure are analyzed while gliding range estimation is considered. Then a new representation of the flight dynamics of an engine-out aircraft is proposed where the space variable is chosen as independent parameter instead of the time variable. This allows to propose a new formulation of the corresponding trajectory optimization problem and to develop a reverse dynamic programming solution technique. Simulation results are displayed and new development perspectives are discussed.

El programa de Ingeniería Mecatrónica adscrito a la Facultad de Ingeniería de la Universidad Autónoma de Bucaramanga, concibe dentro de su plan de estudios un grupo de cursos que conforman el núcleo integrador, dentro del cual se desarrolla un proyecto integrador. El núcleo integrador se orienta a la creación de competencias profesionales (i.e relacionadas con el saber hacer) y personales (i.e relacionadas con el ser) en cada una de las líneas de conocimiento propias (automatización, diseño y electrónica) del programa que permitan a los estudiantes dar solución a una problemática propuesta bajo una mirada crítica de la realidad social, científica y profesional. El proyecto integrador se caracteriza por abordar el desarrollo progresivo del aprendizaje basado en la aplicación de los conceptos fundamentales en cada nivel de la carrera. Para lograrlo, en el inicio de cada semestre/ciclo, el profesorado responsable selecciona temáticas para llevar a cabo el proyecto, las cuales representan un desafío para el estudiante ya que debe dar solución de manera integral, versátil, amigable al usuario/cliente, de fácil implementación y factible económicamente (i.e bajo costo), incluyendo a su vez, el uso de las TIC, programación y dispositivos electromecánicos (i.e PLC’s, Actuadores etc.), además de establecer un plan (i.e cronograma) y metodología realizable en el lapso de tiempo de un semestre/ciclo. Este trabajo pretende reportar la experiencia de aula en la aplicación del núcleo integrador a través de su proyecto integrador ubicado en el séptimo semestre y coordinado en el curso de Automatización Industrial, en este curso específicamente, dada la convergencia y sinergia de las cuatro áreas de la mecatrónica: mecánica, computación, electrónica y control, generando subáreas como simulación, modelamiento de sistemas, sistemas embebidos y sensores. Es un espacio de formación investigativa que involucra a los estudiantes con la realidad, centra su acción en la elaboración de soluciones integrales a un problema, integrando sus conocimientos y desarrollando habilidades para obtener resultados innovadores. Además, pretende mostrar la contextualización sobre el núcleo integrador en el programa de Ingeniería Mecatrónica, y la metodología usada desde el diseño conceptual hasta el desarrollo del producto final teniendo en cuenta estrategias de formación tales como aprendizaje basado en proyectos o aprendizaje basado en retos del núcleo integrador. Finalmente, a través de un análisis de fortalezas y debilidades que han sido observadas a partir de la rúbrica utilizada para la evaluación del núcleo integrador y establecer el plan de mejora para el curso.