253 research outputs found
Development of BCI Based Wheelchair Using Steady State Visual Evoked Potential
This paper shows a Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface (BCI) framework to control a wheelchair in forward, in reverse, left, right and in stop positions. Four diverse glinting frequencies in low recurrence area were utilized to evoke the SSVEPs and were shown on a Liquid Crystal Display (LCD) screen utilizing LabVIEW. The Electroencephalogram (EEG) signals recorded from the occipital district were initially fragmented into 1 second window and elements were removed by utilizing Fast Fourier Transform (FFT). Three distinct classifiers, two in light of Artificial Neural Network (ANN) and one taking into account Support Vector Machine (SVM) were planned and contrasted with yield better exactness. Ten subjects were taken part in the analysis and the precision was figured by considering the quantity of right location delivered while performing a predefined development succession. One-Against-All (OAA) based multiclass SVM classifier indicated preferred exactness over the ANN classifiers
Effects of orthographic consistency and homophone density on Chinese spoken word recognition
Studies of alphabetic language have shown that orthographic knowledge influences phonological processing during spoken word recognition. This study utilized the Event-Related Potentials (ERPs) to differentiate two types of phonology-to-orthography (P-to-O) mapping consistencies in Chinese, namely homophone density and orthographic consistency. The ERP data revealed an orthographic consistency effect in the frontal-centrally distributed N400, and a homophone density effect in central-posteriorly distributed late positive component (LPC). Further source analyses using the standardized low-resolution electromagnetic tomography (sLORETA) demonstrated that the orthographic effect was not only localized in the frontal and temporal-parietal regions for phonological processing, but also in the posterior visual cortex for orthographic processing, while the homophone density effect was found in middle temporal gyrus for lexical-semantic selection, and in the temporal-occipital junction for orthographic processing. These results suggest that orthographic information not only shapes the nature of phonological representations, but may also be activated during on-line spoken word recognition
A Novel Approach Of Independent Brain-computer Interface Based On SSVEP
Durante os últimos dez anos, as Interfaces Cérebro Computador (ICC)
baseadas em Potenciais Evocados Visuais de Regime Permanente (SSVEP) têm
chamado a atenção de muitos pesquisadores devido aos resultados promissores e as altas taxas de precisão atingidas. Este tipo de ICC permite que pessoas com dificuldades motoras severas possam se comunicar com o mundo exterior através da modulação da atenção visual a luzes piscantes com frequência determinada. Esta Tese de Doutorado tem o intuito de desenvolver um novo enfoque dentro das chamadas ICC Independentes, nas quais os usuários não necessitam executar tarefas neuromusculares para seleção visual de objetivos específicos, característica que a distingue das tradicionais ICCs-SSVEP. Assim, pessoas com difculdades motoras severas, como pessoas com Esclerose Lateral Amiotrófca (ELA), contam com uma nova alternativa de se comunicar através de sinais cerebrais. Diversas contribuições foram realizadas neste trabalho, como, por exemplo, melhoria do algoritmo extrator de características, denominado Índice de Sincronização Multivariável (ou MSI,
do Inglês), para a detecção de potenciais evocados; desenvolvimento de um novo método de detecção de potenciais evocados através da correlação entre modelos multidimensionais (tensores); o desenvolvimento do primeiro estudo sobre a influência de estímulos coloridos na detecção de SSVEPs usando LEDs; a aplicação do conceito de Compressão na detecção de SSVEPs; e, fnalmente, o desenvolvimento de uma nova ICC independente que utiliza o enfoque de Percepção Fundo-Figura (ou FGP, do Inglês)
Advances in Robot Navigation
Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics
Semi-autonomous robotic wheelchair controlled with low throughput human- machine interfaces
For a wide range of people with limited upper- and lower-body mobility, interaction with robots remains a challenging problem. Due to various health conditions, they are often unable to use standard joystick interface, most of wheelchairs are equipped with. To accommodate this audience, a number of alternative human-machine interfaces have been designed, such as single switch, sip-and-puff, brain-computer interfaces. They are known as low throughput interfaces referring to the amount of information that an operator can pass into the machine. Using them to control a wheelchair poses a number of challenges. This thesis makes several contributions towards the design of robotic wheelchairs controlled via low throughput human-machine interfaces: (1) To improve wheelchair motion control, an adaptive controller with online parameter estimation is developed for a differentially driven wheelchair. (2) Steering control scheme is designed that provides a unified framework integrating different types of low throughput human-machine interfaces with an obstacle avoidance mechanism. (3) A novel approach to the design of control systems with low throughput human-machine interfaces has been proposed. Based on the approach, position control scheme for a holonomic robot that aims to probabilistically minimize time to destination is developed and tested in simulation. The scheme is adopted for a real differentially driven wheelchair. In contrast to other methods, the proposed scheme allows to use prior information about the user habits, but does not restrict navigation to a set of pre-defined points, and parallelizes the inference and motion reducing the navigation time. (4) To enable the real time operation of the position control, a high-performance algorithm for single-source any-angle path planning on a grid has been developed. By abandoning the graph model and introducing discrete geometric primitives to represent the propagating wave front, we were able to design a planning algorithm that uses only integer addition and bit shifting. Experiments revealed a significant performance advantage. Several modifications, including optimal and multithreaded implementations, are also presented
Physical Diagnosis and Rehabilitation Technologies
The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices
On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks
A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy.
Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features.
The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world
Cognitive-developmental learning for a humanoid robot : a caregiver's gift
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 319-341).(cont.) which are then applied to developmentally acquire new object representations. The humanoid robot therefore sees the world through the caregiver's eyes. Building an artificial humanoid robot's brain, even at an infant's cognitive level, has been a long quest which still lies only in the realm of our imagination. Our efforts towards such a dimly imaginable task are developed according to two alternate and complementary views: cognitive and developmental.The goal of this work is to build a cognitive system for the humanoid robot, Cog, that exploits human caregivers as catalysts to perceive and learn about actions, objects, scenes, people, and the robot itself. This thesis addresses a broad spectrum of machine learning problems across several categorization levels. Actions by embodied agents are used to automatically generate training data for the learning mechanisms, so that the robot develops categorization autonomously. Taking inspiration from the human brain, a framework of algorithms and methodologies was implemented to emulate different cognitive capabilities on the humanoid robot Cog. This framework is effectively applied to a collection of AI, computer vision, and signal processing problems. Cognitive capabilities of the humanoid robot are developmentally created, starting from infant-like abilities for detecting, segmenting, and recognizing percepts over multiple sensing modalities. Human caregivers provide a helping hand for communicating such information to the robot. This is done by actions that create meaningful events (by changing the world in which the robot is situated) thus inducing the "compliant perception" of objects from these human-robot interactions. Self-exploration of the world extends the robot's knowledge concerning object properties. This thesis argues for enculturating humanoid robots using infant development as a metaphor for building a humanoid robot's cognitive abilities. A human caregiver redesigns a humanoid's brain by teaching the humanoid robot as she would teach a child, using children's learning aids such as books, drawing boards, or other cognitive artifacts. Multi-modal object properties are learned using these tools and inserted into several recognition schemes,by Artur Miguel Do Amaral Arsenio.Ph.D
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