Brain Machine Interface Using Emotiv EPOC to Control Robai Cyton Robotic Arm

The initial framework for an electroencephalography (EEG) thought recognition software suite is developed, built, and tested. This suite is designed to recognize human thoughts and pair them to actions for controlling a robotic arm. Raw EEG brain activity data is collected using an Emotiv EPOC headset. The EEG data is processed through linear discriminant analysis (LDA), where an intended action is identified. The EEG classification suite is being developed to increase the number of distinct actions that can be identified compared to the Emotiv recognition software. The EEG classifier was able to correctly distinguish between two separate physical movements. Future goals for this research include recognition of more gestures, and enabling of real time processing

An implementation of electroencephalogram signals acquisition to control manipulator through brain computer interface

Brain computer interface (BCI) technology can be used to design a robotic arm whose decision would be based on the brain activity and brain signals. This proposed design can be more beneficial for the paralyzed people and the patients who are suffering from Amyotrophic lateral sclerosis (ALS), Locked in syndrome (LIS), or neurodegenerative disease. Due to these disease patients would not be able to hold and grip the objects properly. Extensive literature review showed that various EEG signal analysis has been completed with the accuracy of 70% to 85%. The suggested solution would be beneficial to the patients in terms of performing every day functions easily like draws opening, holding dishes and opening and closing of doors as well with more accuracy. In the proposed research electroencephalogram signals were observed and used to classify the type of the motion. Data acquisition comprised of three stages amplification can be considered as cost effective signal conditioning. High pass filter, low pass filter and then converted from analog to digital. Open vibe software was used to design the basic neuron scenario for the brain signals and then classified into alpha and beta waves. Robotic arm movement was based on the alpha and beta waves were performed precisely. Simulated results proved that proposed EEG signals acquisition performed better and can be acknowledged as cost effective. Researchers showed the successful execution of the brain wave signal classification with less false alarm rate for the robotic arm movement by modulation, digitization of the brain signal. Moreover, comparative analysis has been performed of Quadratic Discriminant analysis, k-NN and Medium Gaussian SVM in terms of accuracy prediction speed and training time. Comparative analysis proved that Medium Gaussian SVM worked better than the other classifiers with the accuracy of 95.8%. It was also proved that Medium Gaussian classifier has the capability to predict 10000 observations per second in 0.75466 training time. © 2019 IEEE

Diseño del sistema de control de un brazo robótico de asistencia a personas discapacitadas

La presente tesis tiene por finalidad el diseño de un sistema para comandar un brazo robótico de asistencia que estará montado sobre una silla de ruedas automatizada, y cuya operación será por medio de señales EEG, con el objetivo de asistir a pacientes postrados con limitaciones de discapacidad muscular en miembros superiores, esclerosis lateral amiotrófica, lesión de la médula espinal, entre otros. El trabajo se enfoca en la implementación de un sistema basado en el procesamiento de señales cerebrales producto de estímulos visuales modulados a frecuencias específicas, con las cuales será posible clasificar y definir comandos de movimientos básicos sobre el brazo robótico. Todo ello con el objetivo de reducir fatigas mentales producto del uso de otras técnicas, como las cognitivas, que requieren mayor esfuerzo de concentración y muchas horas de entrenamiento previo para su correcto funcionamiento. Así mismo, la investigación muestra los criterios para la implementación del sistema de generación de estímulos visuales y resultados de los experimentos durante la adquisición, el procesamiento y clasificación de las señales recolectadas a partir de un dispositivo BCI portátil, con características limitadas en precisión y ancho de banda.Tesi

Hybrid wheelchair controller for handicapped and quadriplegic patients

In this dissertation, a hybrid wheelchair controller for handicapped and quadriplegic patient is proposed. The system has two sub-controllers which are the voice controller and the head tilt controller. The system aims to help quadriplegic, handicapped, elderly and paralyzed patients to control a robotic wheelchair using voice commands and head movements instead of a traditional joystick controller. The multi-input design makes the system more flexible to adapt to the available body signals. The low-cost design is taken into consideration as it allows more patients to use this system

Utilização de sensores bioelétricos em modelos de redes de Petri IOPT - Aplicação ao controlo de um quadricóptero

Até hoje, o uso de eletroencefalogramas, eletromiogramas e eletro-oculogramas foram direcionados para auxiliar, aumentar ou reparar a ação cognitiva humana ou as suas funções sensoriais ou motoras, mantendo-se um distanciamento em relação às restantes aplicações onde estas tecnologias po-dem ser potencialmente utilizadas. Nesta dissertação foi implementado um controlador especificado através de um modelo de redes de Petri IOPT aplicado ao controlo de um quadricópte-ro, utilizando um conjunto de sinais bioelétricos obtidos de um capacete EPOC, fabricado pela Emotiv. Foi desenvolvida uma biblioteca de funções de aquisição e processamento dos sinais bioelétricos apta a ser reutilizada em diferentes aplicações. Como resultado, os controladores especificados em modelos de redes de Petri IOPT e gerados pelo ambiente de desenvolvimento IOPT-Tools podem utilizar sinais giroscópicos ou bioelétricos (estes dependentes de expressões fa-ciais, estados de emoção ou pensamentos específicos), para definir as ações de controlo de qualquer dispositivo ou aparelho eletrónico