Eduardo Santamaría-Vázquez

Universidad de Valladolid | UVA · Department of Theory of Signal and Communications and Telematic Engineering

Brain-computer interface (BCI) spellers based on event related potentials (ERPs) are intrinsically synchronous systems. Therefore, selections are constantly made, even when users are not paying attention to the stimuli. This poses a major limitation in real-life applications, in which an asynchronous control is required. The aim of this study is to...

In this study, we present a novel deep learning architecture for brain-computer interfaces based on event related potentials (ERP). The topology of the neural network combines convolutional and recurrent layers in order to learn high-level spatial and temporal features. Specifically, our model uses a convolutional layer, intended to detect spatial...

Background Public speaking is an indispensable skill that can profoundly influence success in both professional and personal spheres. Regrettably, managing anxiety during a speech poses a significant challenge for many of the population. This research assessed the impacts of a Corp-Oral program, designed to manage public speaking anxiety in univers...

Code-modulated visual evoked potentials (c-VEPs) are an innovative control signal utilized in brain-computer interfaces (BCIs) with promising performance. Prior studies on steady-state visual evoked potentials (SSVEPs) have indicated that the spatial frequency of checkerboard-like stimuli influences both performance and user experience. Spatial fre...

Code-modulated visual evoked potentials (c-VEPs) have potential as a reliable and non-invasive control signal for brain-computer interfaces (BCIs). However, these systems need to become more user-friendly. Non-binary codes have been proposed to reduce visual fatigue, but there is still a lack of adaptive methods to shorten trial durations. To addre...

Brain-computer interfaces (BCI) based on code-modulated visual evoked potentials (c-VEP) have shown great potential for communication and device control. These systems encode each command using different sequences of visual stimuli. Normally, the stimulation pattern is binary (i.e., black and white), but non-binary stimuli sequences with different...

Introduction and objective Video games are crucial to the entertainment industry, nonetheless they can be challenging to access for those with severe motor disabilities. Brain-computer interfaces (BCI) systems have the potential to help these individuals by allowing them to control video games using their brain signals. Furthermore, multiplayer BCI...

Background and objective. Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It is implemented with a closed-loop brain-computer interface (BCI) system that analyzes the user’s brain activity in real-time and provides continuous feedback. This paradigm is of great interest due to its potential as a non-p...

Background and objective: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is th...

El Neurofeedback (NF) es una de las principales aplicaciones de los sistemas Brain-Computer Interfaces (BCI). Esta técnica busca inducir cambios en la actividad cerebral del usuario a través del aprendizaje de la modulación voluntaria de los ritmos neuronales. En este trabajo se presenta un análisis exploratorio de los efectos del NF desde una pers...

In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym . Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming inter-subject variability and reducing BCI inefficiency, which has been estimated to affect 10-50...

Background and objective: Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requiring the intervention of a supervisor when the user wishes to turn his attention away from the BCI s...

Motor and cognitive disabilities may lead to communication difficulties, exacerbated by the intelligibility of speech and gesture. In this context, brain–computer interfaces (BCIs) can be viewed as novel augmentative and alternative communication technologies to assist these people. Despite the extensive research in BCIs during the last decades, po...

Neurofeedback training (NFT) allows to self-regulate neural activity, having application on a wide range of disorders to improve cognitive functions. This work was aimed at designing, developing and testing a novel NFT platform to prevent cognitive decline due to normal ageing in elderly population. A closed-loop brain-computer interface based on e...

Many brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful channel selection increases the performance and users’ comfort while reducing the cost of the system. Evolutionary meta-heuristics, which have demonstrated their usefulness in solving complex problems, have not been fully...

Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes...

Objective. Code-modulated visual evoked potentials (c-VEP) have been consolidated in recent years as robust control signals capable of providing non-invasive brain–computer interfaces (BCIs) for reliable, high-speed communication. Their usefulness for communication and control purposes has been reflected in an exponential increase of related articl...

Las interfaces cerebro-ordenador (BCI) basadas en potenciales evocados visuales modulados mediante código (c-VEP) están marcando un antes y un después este ámbito. Los comandos a seleccionar se iluminan de acuerdo a una secuencia pseudo-aleatoria desfasada con distintos retardos. Como las iluminaciones de los comandos adyacentes también son percibi...

This study aims at assessing the usefulness of deep learning to enhance the diagnostic ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea (OSA). A total of 3196 blood oxygen saturation (SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) signals fro...

Objective: The aim of this study was to solve one of the current limitations for the characterization of the brain network in the Alzheimer's disease (AD) continuum. Nowadays, frequency-dependent approaches have reached contradictory results depending on the frequency band under study, tangling the possible clinical interpretations. Approach: To...

In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, inclu...

Introducción y objetivos: La hipoacusia supone un severo hándicap para cualquier profesional cuya actividad se base en el reconocimiento de sonidos. En el caso de profesionales sanitarios, la auscultación constituye una actividad rutinaria y el padecimiento de hipoacusia la limita en grado variable en función de la severidad de la misma. Aquellos p...

En este estudio se evalúa la utilidad de una arquitectura deep learning basada en módulos Inception para mejorar la capacidad diagnóstica de la señal de saturación de oxígeno en sangre (SpO2) en la ayuda al diagnóstico de la apnea obstructiva del sueño (AOS) infantil. Estudios recientes demandan la aplicación de nuevas arquitecturas de deep learnin...

Motor and cognitive disabilities may lead to communication difficulties, exacerbated by the intelligibility of speech and gesture. In this context, brain-computer interfaces (BCIs) can be viewed as novel augmentative and alternative communication technologies to assist these people. Despite the extensive research in BCIs during the last decades, po...

There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective d...

Figure 5 of the original paper contains errors [...]

Introducción y objetivos. La hipoacusia supone un severo hándicap para cualquier profesional cuya actividad se base en el reconocimiento de sonidos. En el caso de profesionales sanitarios, la auscultación constituye una actividad rutinaria y el padecimiento de hipoacusia la limita en grado variable en función de la severidad de la misma. Aquellos p...

Brain–computer interfaces (BCIs) have emerged as novel technologies that can bridge the accessibility gap for the disabled by monitoring their brain signals and translating their intentions into application commands. Despite the recent development of the Internet, web browsers are not yet adapted to an assistive context. The objective of this chapt...

Los sistemas Brain-Computer Interface (BCI) permiten la comunicación en tiempo real entre el cerebro y el entorno midiendo la actividad neuronal, sin la necesidad de que intervengan músculos o nervios periféricos. En la práctica, normalmente se emplea el electroencefalograma (EEG) para registrar la actividad cerebral, debido a que se realiza con un...

In this study, a new automated noise rejection algorithm, the SOurce-estimate-Utilizing Noise-Discarding algorithm (SOUND), was evaluated on magnetoencephalographic (MEG) resting-state signals in order to select its optimal configuration parameters. Different values of the epoch length and the regularization parameter λ0 were assessed in three scen...

Brain–computer interfaces (BCI) have traditionally worked using synchronous paradigms. In recent years, much effort has been put into reaching asynchronous management, providing users with the ability to decide when a command should be selected. However, to the best of our knowledge, entropy metrics have not yet been explored. The present study has...

En este estudio se presenta una Interfaz Cerebro-Computadora que permite controlar las principales funcionalidades de un teléfono móvil de última generación mediante las ondas cerebrales del usuario. El sistema utiliza los potenciales evocados P300, generados a través de un paradigma "odd-ball" visual, para determinar las intenciones del usuario en...

This study presents an asynchronous P300-based Brain–Computer Interface (BCI) system for controlling social networking features of a smartphone. There are very few BCI studies based on these mobile devices and, to the best of our knowledge, none of them supports networking applications or are focused on an assistive context, failing to test their s...

Nowadays, smartphones are essential parts of our lives. The wide range of functionalities that they offer to us, from calling, taking photos, sharing information or contacting with people, have contributed to make them a useful tool. However, its accessibility remains restricted to disabled people that are unable to control their motor functions. I...