Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

The human brain is considered as one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: a headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. This chip offers an advanced filtering technology that has a high noise immunity along with an embedded Bluetooth module using which the acquired signal is transferred wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in real-time and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI.  The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The work presented here gives a basic understanding of the functionality of a BCI system, and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis

INTERFACCE NEURALI PER APPLICAZIONI BIOMEDICHE

Le interfacce neurali (BCI) si occupano di realizzare un canale di comunicazione artificiale diretto tra cervello e un dispositivo esterno attraverso l’uso di appositi sensori e senza coinvolgere processi motori. Le BCI quindi registrano, amplificano ed interpretano gli impulsi nervosi della corteccia per poi fornire un output specifico. Tutto ciò è possibile grazie al fatto che in ogni istante il nostro cervello genera milioni di impulsi nervosi, cioè i segnali elettrici costituenti le onde cerebrali. Tali onde cerebrali possono essere rilevate da elettrodi metallici posizionati in prossimità delle zone cerebrali di interesse, a diversi livelli di profondità. Da qui, una cruciale distinzione tra le BCI invasive, semi-invasive e non invasive; rispettivamente, le prime sono installate in una parte più profonda del cervello mentre le seconde sulla superficie di esso e, infine, le ultime non necessitanti un’operazione chirurgica. Nel contesto dell’ingegneria biomedica e della neuroingegneria, il ruolo svolto dalle BCI è nella direzione di sistemi di supporto funzionale e ausilio per persone con disabilità, con l’obiettivo di sviluppare interfacce neurali in grado di studiare con dettaglio i principi di funzionamento del cervello e delle malattie che lo colpiscono, per mettere a punto dispositivi per diagnosi e terapia di malattie come epilessia, Alzheimer e Parkinson

Diseño, desarrollo y evaluación de un sistema Brain Computer Interface (BCI) basado en Steady-State Visual Evoked Potentials (SSVEPs))

En 1929, Hans Berger desarrolló el elenctroencefalograma (EEG). Desde entonces, el estudio de las señales biómedicas, y en concreto del EEG, ha progresado notablemente. A partir de este descubrimiento, las ondas cerebrales, las cuales eran completamente desconocidas, comenzaron a incluirse para diagnosticar enfermedades como la epilepsia o trastornos del sueño, además de para la investigación y compresión del cerebro. Posteriormente, se comenzó a especular con la utilización de las señales EEG para desarrollar un sistema de comunicaciones entre el cerebro y el medio sin la intervención de músculos y nervios periféricos. Este tipo de sistemas no aparecieron hasta el año 1977, cuando el Dr. Jacques Vidal bautizó el primero con el nombre de Brain-Computer Interface (BCI). Dichos sistemas permiten el control de dispositivos a partir de la monitorización de la actividad cerebral y de la traducción de las intenciones del usuario en comandos de dispositivo. Este trabajo tiene el objetivo de diseñar, desarrollar y evaluar un sistema BCI basado en Steady-State Visual Evoked Potentials (SSVEPs). La aplicación fue desarrollada mediante la plataforma MEDUSA, creada por el Grupo de Ingeniería Biomédica de la Universidad de Valladolid. Para ello se implementaron en Python tanto la interfaz gráfica de la aplicación como los métodos de procesado de señal. El sistema BCI bajo estudio se trata de un speller que permite seleccionar comandos, representados en celdas de una matriz, mediante la detección en el EEG de SSVEPs. Estos últimos son provocados por estímulos visuales a una cierta frecuencia de estimulación. Tras realizar una revisión del estado del arte, se concluye que la mejor manera de conseguir dicho objetivo es mediante el paradigma Joint Frequency-Phase Coding y el método de procesado Canonical Correlation Analysis. Una vez desarrollada, la aplicación fue evaluada por cinco sujetos sanos que relizaron varias tareas en una única sesión. Los resultados obtenidos para la mayoría de los sujetos fueron satisfactorios, con una precisión media del 74,06% bajo condiciones controladas en un laboratorio. Tras la realización de las tareas, los sujetos de estudio completaron un cuestionario de satisfacción que permitió conocer su opinión del sistema implementado y realimentar el proyecto con sus sugerencias. De entre las ideas extraidas, destacan las sugerencias de mejora de la interfaz gráfica y de los métodos de procesado de señal.In 1929, Hans Berger developed the elenctroencephalogram (EEG). Since then, the study of biomedical signals, and in particular of the EEG, has progressed significantly. From this discovery, brain waves, which were completely unknown, were increasingly used to diagnose diseases such as epilepsy or sleep disorders, as well as for the research and compression of the brain. After that, investigators began to suggest the use of EEG signals to develop a communication system between the brain and the environment without the intervention of muscles and peripheral nerves. This type of system did not appear until 1977, when Dr. Jacques Vidal baptized the first one with the name of Brain-Computer Interface (BCI). These systems allow the control of devices by monitoring the brain activity and translating the user's intentions into device commands. This work aims to design, develop and evaluate a BCI system based on Steady-State Visual Evoked Potentials (SSVEPs). The application was developed using the MEDUSA platform, created by the Biomedical Engineering Group of the University of Valladolid. For this purpose, both the application's graphic interface and the signal processing methods were implemented in Python. The BCI system under study is a speller that allows selecting commands, represented in cells of a matrix, through the detection in the EEG of SSVEPs. The latter are caused by visual stimuli at a certain stimulation frequency. After a review of the state of the art, it is concluded that the best way to achieve this goal is through the Joint Frequency-Phase Coding paradigm and the processing method Canonical Correlation Analysis. Once developed, the application was evaluated by five healthy subjects who performed several tasks in a single session. The results obtained for most of the subjects were satisfactory, with an average accuracy of 74.06% under controlled conditions in a laboratory. After the completion of the tasks, the study subjects completed a satisfaction questionnaire that allowed to know their opinion about the implemented system and to feedback the project with their suggestions. Among the ideas extracted, suggestions for improving the graphic interface and signal processing methods stand out.Grado en Ingeniería de Tecnologías de Telecomunicació