Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly

Producción CientíficaNeurofeedback training (NFT) has shown to be promising and useful to rehabilitate cognitive functions. Recently, brain-computer interfaces (BCIs) were used to restore brain plasticity by inducing brain activity with a NFT. In our study, we hypothesized that a NFT with a motor imagery-based BCI (MI-BCI) could enhance cognitive functions related to aging effects. To assess the effectiveness of our MI-BCI application, 63 subjects (older than 60 years) were recruited. This novel application was used by 31 subjects (NFT group). Their Luria neuropsychological test scores were compared with the remaining 32 subjects, who did not perform NFT (control group). Electroencephalogram (EEG) changes measured by relative power (RP) endorsed cognitive potential findings under study: visuospatial, oral language, memory, intellectual and attention functions. Three frequency bands were selected to assess cognitive changes: 12, 18, and 21 Hz (bandwidth 3 Hz). Significant increases (p<0.01) in the RP of these frequency bands were found. Moreover, results from cognitive tests showed significant improvements (p<0.01) in four cognitive functions after performing five NFT sessions: visuospatial, oral language, memory, and intellectual. This established evidence in the association between NFT performed by a MI-BCI and enhanced cognitive performance. Therefore, it could be a novel approach to help elderly people.Ministerio de Economía y Competitividad (TEC2014-53196)Junta de Castilla y León (VA059U13

Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems

[EN] Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ. The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level

Real-world human gender classification from oral region using convolutional neural netwrok

Gender classification is an important biometric task. It has been widely studied in the literature. Face modality is the most studied aspect of human-gender classification. Moreover, the task has also been investigated in terms of different face components such as irises, ears, and the periocular region. In this paper, we aim to investigate gender classification based on the oral region. In the proposed approach, we adopt a convolutional neural network. For experimentation, we extracted the region of interest using the RetinaFace algorithm from the FFHQ faces dataset. We achieved acceptable results, surpassing those that use the mouth as a modality or facial sub-region in geometric approaches. The obtained results also proclaim the importance of the oral region as a facial part lost in the Covid-19 context when people wear facial mask. We suppose that the adaptation of existing facial data analysis solutions from the whole face is indispensable to keep-up their robustness

Mejora de una asignatura para la formación del profesorado en programación basada en bloques

Uno de los principales retos para la introducción de una materia obligatoria de informática en niveles educativos preuniversitarios es la falta de profesorado formado en informática. En nuestra universidad ofrecemos un máster para formar profesores en competencia digital y programación. La asignatura “Programación y Pensamiento Computacional I” presenta una introducción a la programación basada en bloques. En el curso académico 2021/22 se realizó un diseño de la asignatura basada en cuatro lenguajes de bloques en orden creciente de complejidad. Aunque los alumnos valoraron muy positivamente la asignatura, se identificaron varias cuestiones mejorables. En la comunicación se presentan los cambios introducidos durante el curso 2022/23, que consisten en la eliminación del lenguaje Code.org, una revisión de los apuntes de Scratch, el desarrollo de nuevos ejercicios de autoestudio para ScratchJr y Scratch, y la transición de Scratch a App Inventor. Se presentan los resultados obtenidos de rendimiento de los alumnos y de aceptación de la asignatura. La asignatura ha consolidado su aceptación por los alumnos, pero los cambios introducidos no han redundado en una mejora apreciable y aún persiste como reto el aprendizaje de los elementos más complejos, principalmente App Inventor.One of the main challenges to introduce informatics as a mandatory subject matter in pre-college education is the lack of teachers adequately trained on informatics. Our university offers master’s studies aimed at teachers’ development in digital competence and computer programming. The course “Programming and computational thinking I” introduces block-based programming. In the academic year 2021/22, the course was designed as a sequence of four languages, in increasing order of complexity. The students rated the course very high, but a few issues were amenable to improvement. In this paper, we present the changes introduced for the academic year 2022/23, comprising the removal of Code.org, re-elaboration of Scratch lecture notes, development of additional self-study exercises for ScratchJr and Scratch, and transition between Scratch and App Inventor. The paper also presents the out-comes obtained on students’ performance and course acceptance. The course is consolidated according to students’ high acceptance. However, the changes introduced did not produce a significant enhancement of acceptance, and learning the most complex elements remains an open challenge, especially App Inventor.Este trabajo se ha financiado con el proyecto de investigación e-Madrid-CM (S2018/TCS-4307) de la Comunidad Autónoma de Madrid y los proyectos-puente PROGRAMA de la Universidad Juan Carlos (M2614 y M3035). El proyecto e-Madrid-CM también está financiado con los fondos estructurales FSE y FEDER