1,333 research outputs found
Ubiquitous Integration and Temporal Synchronisation (UbilTS) framework : a solution for building complex multimodal data capture and interactive systems
Contemporary Data Capture and Interactive Systems (DCIS) systems are tied in with various
technical complexities such as multimodal data types, diverse hardware and software
components, time synchronisation issues and distributed deployment configurations. Building
these systems is inherently difficult and requires addressing of these complexities before the
intended and purposeful functionalities can be attained. The technical issues are often
common and similar among diverse applications.
This thesis presents the Ubiquitous Integration and Temporal Synchronisation (UbiITS)
framework, a generic solution to address the technical complexities in building DCISs. The
proposed solution is an abstract software framework that can be extended and customised to
any application requirements. UbiITS includes all fundamental software components,
techniques, system level layer abstractions and reference architecture as a collection to enable
the systematic construction of complex DCISs.
This work details four case studies to showcase the versatility and extensibility of UbiITS
framework’s functionalities and demonstrate how it was employed to successfully solve a
range of technical requirements. In each case UbiITS operated as the core element of each
application. Additionally, these case studies are novel systems by themselves in each of their
domains. Longstanding technical issues such as flexibly integrating and interoperating
multimodal tools, precise time synchronisation, etc., were resolved in each application by
employing UbiITS. The framework enabled establishing a functional system infrastructure in
these cases, essentially opening up new lines of research in each discipline where these
research approaches would not have been possible without the infrastructure provided by the
framework. The thesis further presents a sample implementation of the framework on a
device firmware exhibiting its capability to be directly implemented on a hardware platform.
Summary metrics are also produced to establish the complexity, reusability, extendibility,
implementation and maintainability characteristics of the framework.Engineering and Physical Sciences Research Council (EPSRC) grants - EP/F02553X/1, 114433 and 11394
Chapter From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities
Interdisciplinary concept of dissipative soliton is unfolded in connection with ultrafast fibre lasers. The different mode-locking techniques as well as experimental realizations of dissipative soliton fibre lasers are surveyed briefly with an emphasis on their energy scalability. Basic topics of the dissipative soliton theory are elucidated in connection with concepts of energy scalability and stability. It is shown that the parametric space of dissipative soliton has reduced dimension and comparatively simple structure that simplifies the analysis and optimization of ultrafast fibre lasers. The main destabilization scenarios are described and the limits of energy scalability are connected with impact of optical turbulence and stimulated Raman scattering. The fast and slow dynamics of vector dissipative solitons are exposed
Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks
Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization
Towards Improving Learning with Consumer-Grade, Closed-Loop, Electroencephalographic Neurofeedback
Learning is an enigmatic process composed of a multitude of cognitive systems that are functionally and neuroanatomically distinct. Nevertheless, two undeniable pillars which underpin learning are attention and memory; to learn, one must attend, and maintain a representation of, an event. Psychological and neuroscientific technologies that permit researchers to “mind-read” have revealed much about the dynamics of these distinct processes that contribute to learning. This investigation first outlines the cognitive pillars which support learning and the technologies that permit such an understanding. It then employs a novel task—the amSMART paradigm—with the goal of building a real-time, closed-loop, electroencephalographic (EEG) neurofeedback paradigm using consumergrade brain-computer interface (BCI) hardware. Data are presented which indicate the current status of consumer-grade BCI for EEG cognition classification and enhancement, and directions are suggested for the developing world of consumer neurofeedback
An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection
Epilepsy is one of the most common neurological diseases globally, affecting
around 50 million people worldwide. Fortunately, up to 70 percent of people
with epilepsy could live seizure-free if properly diagnosed and treated, and a
reliable technique to monitor the onset of seizures could improve the quality
of life of patients who are constantly facing the fear of random seizure
attacks. The scalp-based EEG test, despite being the gold standard for
diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled
professionals for operation, and is discomforting for users. In this paper, we
propose EarSD, a novel lightweight, unobtrusive, and socially acceptable
ear-worn system to detect epileptic seizure onsets by measuring the
physiological signals from behind the user's ears. EarSD includes an integrated
custom-built sensing, computing, and communication PCB to collect and amplify
the signals of interest, remove the noises caused by motion artifacts and
environmental impacts, and stream the data wirelessly to the computer or mobile
phone nearby, where data are uploaded to the host computer for further
processing. We conducted both in-lab and in-hospital experiments with epileptic
seizure patients who were hospitalized for seizure studies. The preliminary
results confirm that EarSD can detect seizures with up to 95.3 percent accuracy
by just using classical machine learning algorithms
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