Diseño y desarrollo de una aplicación para navegar por Internet mediante sistemas Brain Computer Interface orientada a personas con grave discapacidad

El electroencefalograma (EEG) fue desarrollado por Hans Berger en 1929 y las ondas cerebrales, hasta entonces desconocidas, fueron investigadas durante el siglo XX como ayuda para el diagnóstico de la epilepsia o diversos trastornos del sueño. Durante este periodo de tiempo se especuló sobre utilizar las señales EEG para desarrollar un sistema de comunicación entre el cerebro y el medio sin la intervención de los mecanismos normales de los nervios y los músculos periféricos. Ese sistema surgió a cargo de Dr. Jacques Vidal en 1977 y se bautizó como Brain Computer Interface (BCI). Estos tipos de sistemas se basan en monitorizar la actividad cerebral y traducir determinadas características, correspondientes a las intenciones del usuario, en comandos de un dispositivo. El objetivo del presente trabajo es el de diseñar y desarrollar una aplicación BCI que permita, a las personas con grave discapacidad, navegar por Internet libremente mediante el uso de sus ondas cerebrales.Grado en Ingeniería de Tecnologías Específicas de Telecomunicació

Brain-computer interface channel selection optimization using meta-heuristics and evolutionary algorithms

Producción CientíficaMany 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 exploited yet in this context. The purpose of the study is two-fold: (1) to propose a novel algorithm to find an optimal channel set for each user and compare it with other existing meta-heuristics; and (2) to establish guidelines for adapting these optimization strategies to this framework. A total of 3 single-objective (GA, BDE, BPSO) and 4 multi-objective (NSGA-II, BMOPSO, SPEA2, PEAIL) existing algorithms have been adapted and tested with 3 public databases: ‘BCI competition III–dataset II’, ‘Center Speller’ and ‘RSVP Speller’. Dual-Front Sorting Algorithm (DFGA), a novel multi-objective discrete method especially designed to the BCI framework, is proposed as well. Results showed that all meta-heuristics outperformed the full set and the common 8-channel set for P300-based BCIs. DFGA showed a significant improvement of accuracy of 3.9% over the latter using also 8 channels; and obtained similar accuracies using a mean of 4.66 channels. A topographic analysis also reinforced the need to customize a channel set for each user. Thus, the proposed method computes an optimal set of solutions with different number of channels, allowing the user to select the most appropriate distribution for the next BCI sessions.Ministerio de Ciencia, Innovación y Universidades (project RTC2019-007350-1)Comisión Europea (project 0702_MIGRAINEE_2_E

Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning

Producción CientíficaBackground 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 system. In order to bring these BCIs into real-life applications, a robust asynchronous control of the system is required through monitoring of user attention. Despite the great importance of this limitation, which prevents the deployment of these systems outside the laboratory, it is often overlooked in research articles. This study was aimed to propose a novel method to solve this problem, taking advantage of deep learning for the first time in this context to overcome the limitations of previous strategies based on hand-crafted features. Methods. The proposed method, based on EEG-Inception, a novel deep convolutional neural network, divides the problem in 2 stages to achieve the asynchronous control: (i) the model detects user’s control state, and (ii) decodes the command only if the user is attending to the stimuli. Additionally, we used transfer learning to reduce the calibration time, even exploring a calibration-less approach. Results. Our method was evaluated with 22 healthy subjects, analyzing the impact of the calibration time and number of stimulation sequences on the system’s performance. For the control state detection stage, we report average accuracies above 91% using only 1 sequence of stimulation and 30 calibration trials, reaching a maximum of 96.95% with 15 sequences. Moreover, our calibration-less approach also achieved suitable results, with a maximum accuracy of 89.36%, showing the benefits of transfer learning. As for the overall asynchronous system, which includes both stages, the maximum information transfer rate was 35.54 bpm, a suitable value for high-speed communication. Conclusions. The proposed strategy achieved higher performance with less calibration trials and stimulation sequences than former approaches, representing a promising step forward that paves the way for more practical applications of ERP-based spellers.Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (grants PID2020-115468RB-I00 and RTC2019-007350-1)Comisión Europea - Fondo Europeo de Desarrollo Regional (cooperation programme Interreg V-A Spain-Portugal POCTEP 2014–2020

An Asynchronous P300-Based Brain-Computer Interface Web Browser for Severely Disabled People

This paper presents an electroencephalo- graphic (EEG) P300-based brain–computer interface (BCI) Internet browser. The system uses the “odd-ball” row-col paradigm for generating the P300 evoked potentials on the scalp of the user, which are immediately processed and translated into web browser commands. There were previous approaches for controlling a BCI web browser. However, to the best of our knowledge, none of them was focused on an assistive context, failing to test their applications with a suitable number of end users. In addition, all of them were synchronous applications, where it was necessary to introduce a “read-mode” command in order to avoid a continuous command selection. Thus, the aim of this study is twofold: 1) to test our web browser with a population of multiple sclerosis (MS) patients in order to assess the usefulness of our proposal to meet their daily communication needs; and 2) to overcome the aforementioned limitation by adding a threshold that discerns between control and non-control states, allowing the user to calmly read the web page without undesirable selections. The browser was tested with sixteen MS patients and five healthy volunteers. Both quantitative and qualitative metrics were obtained. MS participants reached an average accuracy of 84.14%, whereas 95.75% was achieved by control subjects. Results show that MS patients can successfully control the BCI web browser, improving their personal autonom

Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience

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 frequency refers to the dimensions of the individual squares comprising the visual stimulus, quantified in cycles (i.e., number of black-white squares pairs) per degree of visual angle. However, the specific effects of this parameter on c-VEP-based BCIs remain unexplored. Therefore, the objective of this study is to investigate the role of spatial frequency of checkerboard-like visual stimuli in a c-VEP-based BCI. Sixteen participants evaluated selection matrices with eight spatial frequencies: C001 (0 c/°, 1×1 squares), C002 (0.15 c/°, 2×2 squares), C004 (0.3 c/°, 4×4 squares), C008 (0.6 c/°, 8×8 squares), C016 (1.2 c/°, 16×16 squares), C032 (2.4 c/°, 32×32 squares), C064 (4.79 c/°, 64×64 squares), and C128 (9.58 c/°, 128×128 squares). These conditions were tested in an online spelling task, which consisted of 18 trials each conducted on a 3×3 command interface. In addition to accuracy and information transfer rate (ITR), subjective measures regarding comfort, ocular irritation, and satisfaction were collected. Significant differences in performance and comfort were observed based on different stimulus spatial frequencies. Although all conditions achieved mean accuracy over 95% after 2.1 s of trial duration, C016 stood out in terms user experience. The proposed condition not only achieved a mean accuracy of 96.53% and 164.54 bits/min with a trial duration of 1.05s, but also was reported to be significantly more comfortable than the traditional C001 stimulus. Since both features are key for BCI development, higher spatial frequencies than the classical black-to-white stimulus might be more adequate for c-VEP systems. Hence, we assert that the spatial frequency should be carefully considered in the development of future applications for c-VEP-based BCIs

MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research

Producción CientíficaBackground 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 the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations. Methods. We followed strict development practices to optimize MEDUSA© for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages. Results. MEDUSA© provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility. Conclusions. MEDUSA© is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación/10.13039/501100011033/' y el Fondo Europeo de Desarrollo Regional (FEDER) grants (PID2020-115468RB-I00 and RTC2019-007350-1

Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs

Producción CientíficaCode-modulated visual evoked potentials (c-VEPs) have marked a milestone in the scientific literature due to their ability to achieve reliable, high-speed brain–computer interfaces (BCIs) for communication and control. Generally, these expert systems rely on encoding each command with shifted versions of binary pseudorandom sequences, i.e., flashing black and white targets according to the shifted code. Despite the excellent results in terms of accuracy and selection time, these high-contrast stimuli cause eyestrain for some users. In this work, we propose the use of non-binary p-ary m-sequences, whose levels are encoded with different shades of gray, as a more pleasant alternative than traditional binary codes. The performance and visual fatigue of these p-ary m-sequences, as well as their ability to provide reliable c-VEP-based BCIs, are analyzed for the first time.Ministerio de Ciencia e Innovación/AEI- FEDER [TED2021-129915B-I00, RTC2019-007350-1 y PID2020-115468RB-I00

ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces

Producción CientíficaNeurofeedback (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-pharmacological and non-invasive alternative to treat non-degenerative brain disorders. Nevertheless, currently available NF frameworks have several limitations, such as the lack of a wide variety of real-time analysis metrics or overly simple training scenarios that may negatively affect user performance. To overcome these limitations, this work proposes ITACA: a novel open-source framework for the design, implementation and evaluation of NF training paradigms.Ministerio de Ciencia e innovación, Agencia Estatal de Investigación y FEDER (PID2020-115468RB-I00, RTC2019-007350-1 y TED2021-129915B-I00

Control asíncrono de sistemas BCI basados en ERP mediante la detección de potenciales evocados visuales de estado estable provocados por los estímulos periféricos del paradigma oddball

[ES] 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 equipo portable, no invasivo y de bajo coste en comparación con otras técnicas disponibles. Una vez adquirida la señal EEG, esta es analizada en tiempo real por un software que determina las intenciones del usuario y las traduce en comandos de la aplicación, proporcionando una realimentación visual o auditiva. En concreto, los sistemas BCI basados en potenciales relacionados con eventos (event related potentials, ERP) utilizan el llamado paradigma oddball. Este paradigma presenta una matriz de comandos, cuyas filas y columnas se iluminan de manera secuencial. Para seleccionar un comando, el usuario debe mirar a fijamente a la celda correspondiente de la matriz. Los estímulos visuales, percibidos con la región central de su campo visual, provocan un ERP en la señal de EEG. Posteriormente, el sistema determina el comando que quiere seleccionar el usuario mediante la detección de estos ERP. Actualmente, una de las mayores limitaciones de los sistemas BCI basados en ERP es que son inherentemente síncronos. La aplicación selecciona un comando después de un número predefinido de iluminaciones, aunque el usuario no esté atendiendo a los estímulos. Esta limitación restringe el uso de estos sistemas en la vida real, donde los usuarios deberían poder dejar de prestar atención a la aplicación para realizar otras tareas sin que se seleccionen comandos indeseados. Esta característica es especialmente importante en aplicaciones BCI enfocadas al aumento de la calidad de vida de personas con grave discapacidad, como navegadores web o sistemas de control de sillas de ruedas. Para resolver esta limitación, es necesario añadir al sistema un método que detecte en tiempo real si el usuario realmente quiere seleccionar un comando. En este estudio presentamos un novedoso método de asincronía para detectar en tiempo real el estado de control del usuario en los sistemas BCI basados en EPR. Con este objetivo, el sistema detecta los potenciales evocados visuales de estado estable (steady-state visual evoked potentials, SSVEP) provocados por los estímulos periféricos del paradigma oddball. Estas ondas son la respuesta oscilatoria que aparece en la señal de EEG cuando se recibe una estimulación repetitiva a una frecuencia constante. Las iluminaciones periféricas del paradigma oddball provocan un SSVEP a la frecuencia de estimulación, que aparece únicamente cuando el usuario está mirando a la matriz. Por tanto, la detección de esta componente permite determinar si el usuario quiere seleccionar un comando o no. El método propuesto ha sido validado de manera offline con 5 sujetos sanos, alcanzando una precisión media en la detección del estado de control del usuario del 99.7% con 15 secuencias de estimulación. Estos resultados sugieren que esta metodología permite un control asíncrono fiable del sistema BCI, lo que es de gran utilidad en aplicaciones para la mejora de la calidad de las personas con grave discapacidad.[EN] Synchronicity is an inherent feature of brain–computer interface (BCI) spellers based on event related potentials (ERPs). These systems always make a selection, even when users are engaged in another task. This represents a great limitation in real-life applications, such as wheelchair control or web browsers, in which an asynchronous control should be a key feature. The aim of this study is to design, develop and test a novel algorithm to discriminate whether the user wants to select a command or is not attending the stimuli. In order to achieve such asynchronous control, our method detects the steady-state visual evoked potentials provoked by nontarget stimuli of ERP-based spellers. The proposed method was validated with offline data from 5 healthy subjects, achieving an average accuracy of 99.7%. Our approach is independent of the ERP classification stage, which reduces inter-session variability. Furthermore, to the best of our knowledge, it is the first algorithm for asynchronous control that does not need to extend the duration of the calibration sessions.Este estudio ha sido financiado por el proyecto DPI2017-84280-R del Ministerio de Ciencia, Innovación y Universidades y FEDER y el proyecto “Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer” (Programa Operativo de Cooperación Transfronteriza España-Portugal, POCTEP, 2014-2020) de la Comisión Europea y FEDER. Eduardo Santamaría Vázquez es beneficiario de una ayuda de Personal Investigador en Formación (PIF) financiada por la Consejería de Educación de la Junta de Castilla y León y el Fondo Social Europeo. Víctor Martínez-Cagigal es beneficiario de una ayuda PIF-UVa de la Universidad de Valladolid.Santamaría-Vázquez, E.; Martínez-Cagigal, V.; Gomez-Pilar, J.; Hornero, R. (2019). Control asíncrono de sistemas BCI basados en ERP mediante la detección de potenciales evocados visuales de estado estable provocados por los estímulos periféricos del paradigma oddball. En 11º Simposio CEA de Bioingeniería. Editorial Universitat Politècnica de València. 86-96. https://doi.org/10.4995/CEABioIng.2019.10022OCS869

Sistema brain-computer inteface de navegación web orientado a personas con grave discapacidad

[Resumen] En este estudio se presenta un navegador web basado en un sistema Brain-Computer Interface (BCI) orientado al uso por parte de personas con grave discapacidad. El control de la aplicación lo gobiernan los potenciales evocados P300, generados por el usuario de manera involuntaria a través de un estímulo odd-ball visual. A diferencia de las aproximaciones anteriores, el navegador desarrollado emplea un umbral que determina la atención del usuario, permitiendo un control asíncrono que no requiere modos de lectura para evitar una selección continua de comandos. La aplicación se ha evaluado con 5 sujetos de control y 16 enfermos de esclerosis múltiple, alcanzando precisiones medias del 95,75% y del 84,14%, respectivamente. Dichos resultados sugieren que el navegador web puede ser adecuado para el uso por parte de personas con grave discapacida