Research regarding electro-oculogram based Human Computer Interface (HCI)

In this article an electro-oculogram (EOG) based Human Computer Interface (HCI) will be presented, in order to control the mouse cursor on the screen of a computer or laptop. Electromyography (EMG) is the domain that employs the activation and deactivation (onset and cessation) of the muscles. EOG is the sub-domain of EMG field that focuses on the human eye’s movements. The EOG bio-signals can be recorded using Ag/AgCl electrodes coupled on the user’s skin and fed into a data acquisition device - an analog-to-digital converter (ADC) in order to be transmitted, filtered and processed on a computer or laptop. We acquired the EOG bio-signals with a 24-bit, 4 channel, 51200 samples/s per channel ADC, made by the National Instruments (N.I.), model NI-9234 industrial ADC, using only 3 recording channels and electrodes. After processing, the program running on the computer or laptop can be used to realize commands or control different applications according to the recorded bio-signals. In our case, this was done, using Artificial Neural Network (ANN) toolbox of MATLAB®. This HCI can be used by perfectly healthy or even by disabled people. In the case of disabled people, these systems can be used to control any electronic device connected to the computer or control the device itself. Applications of this type of HCIs can be Internet browsing, mail writing, word file editing, etc. This system is meant to offer a new way of computer control - other than the existing standard communication and/or control possibilities (like keyboard and/or mouse)

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37–52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model

Real-Time Control of a Video Game Using Eye Movements and Two Temporal EEG Sensors

EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time control of video game with eye movements for asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets to detect the instance of eye movement and time-series characteristics to distinguish between six classes of eye movement. A control interface was developed to test the proposed algorithm in real-time experiments with opened and closed eyes. Using visual feedback, a mean classification accuracy of 77.3% was obtained for control with six commands. And a mean classification accuracy of 80.2% was obtained using auditory feedback for control with five commands. The algorithm was then applied for controlling direction and speed of character movement in two-dimensional video game. Results showed that the proposed algorithm had an efficient response speed and timing with a bit rate of 30 bits/min, demonstrating its efficacy and robustness in real-time control

Biosegnali del sistema visivo

Il sistema nervoso permette agli esseri umani e alle altre specie viventi che ne sono provviste di interagire con l’ambiente circostante. I neuroni sono le unità funzionali di tale sistema e comunicano tra loro attraverso segnali elettrici e chimici. Il sistema visivo è strettamente connesso al sistema nervoso centrale, esso permette la trasduzione delle radiazioni luminose captate da milioni di fotorecettori in impulsi nervosi elaborati in molteplici zone dell'encefalo. Per comprendere il funzionamento e lo stato di tale sistema è possibile acquisire segnali come l’elettroocuologramma (EOG), l’elettroretinogramma (ERG), l’elettroecefalogramma (EEG), dal quale è possibile estrarre i potenziali evocati visivi (VEP), e le bioimmagini retiniche. Il corretto funzionamento del sistema visivo può essere compromesso da molteplici patologie spesso incurabili. Nel corso dell’ultimo secolo la ricerca scientifica ha ottenuto notevoli risultati nel campo dell’elettrostimolazione della via visiva. Al momento sono in via di sviluppo e in fase di test progetti di protesi retiniche e impianti neurali che promettono di ristabilire in parte le capacità visive dei soggetti colpiti da patologie irreversibili

Implementação de uma Interface Homem-máquina baseada em Eletroculografia

A constante evolução da tecnologia permitiu ao ser humano a utilização de dispositivos electrónicos nas suas rotinas diárias. Estas podem ser afetadas quando os utilizadores sofrem de deficiências ou doenças que afetam as suas capacidades motoras. Com o intuito de minimizar este obstáculo surgiram as Interfaces Homem-Computador (HCI). É neste panorama que os sistemas HCI baseados em Eletroculografia (EOG) assumem um papel preponderante na melhoria da qualidade de vida destes indivíduos. A Eletroculografia é o resultado da aquisição do movimento ocular, que pode ser adquirido através de diversos métodos. Os métodos mais convencionais utilizam elétrodos de superfície para aquisição dos sinais elétricos, ou então, utilizam sistemas de gravação de vídeo, que gravam o movimento ocular. O objetivo desta tese é desenvolver um sistema HCI baseado em Eletroculografia, que adquire o sinal elétrico do movimento ocular através de elétrodos de superfície. Para tal desenvolveu-se um circuito eletrónico para a aquisição do sinal de EOG, bem como um algoritmo em Python para análise do mesmo. O circuito foi desenvolvido recorrendo a seis módulos diferentes, cada um deles com uma função específica. Para cada módulo foi necessário desenhar e implementar placas de circuito impresso, que quando conectadas entre si permitem filtrar, amplificar e digitalizar os sinais elétricos, adquiridos através de elétrodos de superfície, originados pelo movimento ocular. O algoritmo criado em Python permite analisar os dados provenientes do circuito e converte-os para coordenadas. Através destas foi possível determinar o sentido e a amplitude do movimento ocular.The evolution of technology has made possible for humans to use different electronic devices in their daily routines. However, for individuals with severe disabilities or diseases that hinder motor skills, some routines may be unattainable. In order to reduce this problem, Human-Computer Interfaces (HCI) have emerged with the purpose of improving the quality of life of those persons, specifically HCI systems based on Electroculography. Electroculography is the result of the signal provided by the movement of the eyes, which can be acquired by different methods. The most usual of them require the use of surface electrodes to read the electric signal, or video-recording devices to record the eye movement. The goal of this thesis is to develop an HCI system based on Electroculography, which acquires the bio-signal from the eye movement through the use of surface electrodes. For such purpose, a modular electronic circuit was created to capture the EOG signal as well as a Python-based algorithm for its analysis. The circuit was built using six different modules, each of them with an unique function. Each module required the design and implementation of printed circuit boards, which when put together allowed the filtering, magnification and scan of the electric signals originated by the ocular movement. The Python-based algorithm allows the analysis of the data from the circuit and transforms it into coordinates. Through these, it's possible to determine the direction and amplitude of the eye movement

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial