Electronic Imaging & the Visual Arts. EVA 2012 Florence

The key aim of this Event is to provide a forum for the user, supplier and scientific research communities to meet and exchange experiences, ideas and plans in the wide area of Culture & Technology. Participants receive up to date news on new EC and international arts computing & telecommunications initiatives as well as on Projects in the visual arts field, in archaeology and history. Working Groups and new Projects are promoted. Scientific and technical demonstrations are presented

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

Electronic Imaging & the Visual Arts. EVA 2013 Florence

Important Information Technology topics are presented: multimedia systems, data-bases, protection of data, access to the content. Particular reference is reserved to digital images (2D, 3D) regarding Cultural Institutions (Museums, Libraries, Palace – Monuments, Archaeological Sites). The main parts of the Conference Proceedings regard: Strategic Issues, EC Projects and Related Networks & Initiatives, International Forum on “Culture & Technology”, 2D – 3D Technologies & Applications, Virtual Galleries – Museums and Related Initiatives, Access to the Culture Information. Three Workshops are related to: International Cooperation, Innovation and Enterprise, Creative Industries and Cultural Tourism

Decentralization in messaging applications with support for contactless interaction

Peer-to-peer communication has increasingly been gaining prevalence in people’s daily lives, with its widespread adoption being catalysed by technological advances. Although there have been strides for the inclusion of disabled individuals to ease communication between peers, people who suffer arm/hand impairments have little to no support in regular mainstream applications to efficiently communicate with other individuals. Additionally, as centralized systems have come into scrutiny regarding privacy and security, the development of alternative, decentralized solutions have increased, a movement pioneered by Bitcoin that culminated in the blockchain technology and its variants. Aiming towards expanding inclusivity in the messaging applications panorama, this project showcases an alternative on contactless human-computer interaction with support for disabled individuals with focus on the decentralized backend counterpart. Users of the application partake in a decentralized network based on a distributed hash table that is designed for secure communication (granted by a custom cryptographic messaging protocol) and exchange of data between peers. Such system is both resilient to tampering attacks and central points of failure (akin to blockchains), as well as having no long-term restrictions regarding scalability prospects, something that is a recurring issue in blockchain-based platforms. The conducted experiments showcase a level of performance similar to mainstream centralized approaches, outperforming blockchain-based decentralized applications on the delay between sending and receiving messages.A comunicação ponto-a-ponto tem cada vez mais ganhado prevalência na vida contemporânea de pessoas, tendo a sua adoção sido catalisada pelos avanços tecnológicos. Embora tenham havido desenvolvimentos relativamente à inclusão de indivíduos com deficiência para facilitar a comunicação entre pessoas, as que sofrem imparidades no braço/mão têm um suporte escasso em aplicações convencionais para comunicar de forma eficiente com outros sujeitos. Adicionalmente, à medida que sistemas centralizados têm atraído ceticismo relativamente à sua privacidade e segurança, o desenvolvimento de soluções descentralizadas e alternativas têm aumentado, um movimento iniciado pela Bitcoin que culminou na tecnologia de blockchain e as suas variantes. Tendo como objectivo expandir a inclusão no panorama de aplicações de messaging, este projeto pretende demonstrar uma alternativa na interação humano-computador sem contacto direto físico e com suporte para indivíduos com deficiência, com foco no componente backend decentralizado. Utilizadores da aplicação são inseridos num sistema decentralizado baseado numa hash table distribuída que foi desenhado para comunicação segura (providenciado por um protocolo de messaging criptográfico customizado) e para troca de dados entre utilizadores. Tal sistema é tanto resiliente a ataques de adulteração de dados como também a pontos centrais de falha (presente em blockains), não tendo adicionalmente restrições ao nível de escabilidade a longo-prazo, algo que é um problem recorrente em plataformas baseadas em blockchain. As avaliações e experiências realizadas neste projeto demonstram um nível de performance semelhante a abordagens centralizadas convencionais, tendo uma melhor prestação que aplicações descentralizadas baseadas em blockchain no que toca à diferença no tempo entre enviar e receber mensagens

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

Approaches for the digital profiling of activities and their applications in design information push

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An Efficient Machine Learning Software Architecture for Internet of Things

Internet of Things (IoT) software is becoming a critical infrastructure for many domains. In IoT, sensors monitor their environment and transfer readings to cloud, where Machine Learning (ML) provides insights to decision-makers. In the healthcare domain, the IoT software designers have to consider privacy, real-time performance and cost in addition to ML accuracy. We propose an architecture that decomposes the ML lifecycle into components for deployment on a two-tier cloud, edge-core. It enables IoT time-series data to be consumed by ML models on edge-core infrastructure, with pipeline elements deployed on any tier, dynamically. The architecture feasibility and ML accuracy are validated with three brain-computer interfaces (BCI) based use-cases. The contributions are two-fold: first, we propose a novel ML-IoT pipeline software architecture that encompasses essential components from data ingestion to runtime use of ML models; second, we assess the software on cognitive applications and achieve promising results in comparison to literature

Adaptive Cognitive Interaction Systems

Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthält zahlreiche Beiträge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert

Relating EEG to continuous speech using deep neural networks: a review

Objective. When a person listens to continuous speech, a corresponding response is elicited in the brain and can be recorded using electroencephalography (EEG). Linear models are presently used to relate the EEG recording to the corresponding speech signal. The ability of linear models to find a mapping between these two signals is used as a measure of neural tracking of speech. Such models are limited as they assume linearity in the EEG-speech relationship, which omits the nonlinear dynamics of the brain. As an alternative, deep learning models have recently been used to relate EEG to continuous speech, especially in auditory attention decoding (AAD) and single-speech-source paradigms. Approach. This paper reviews and comments on deep-learning-based studies that relate EEG to continuous speech in AAD and single-speech-source paradigms. We point out recurrent methodological pitfalls and the need for a standard benchmark of model analysis. Main results. We gathered 29 studies. The main methodological issues we found are biased cross-validations, data leakage leading to over-fitted models, or disproportionate data size compared to the model's complexity. In addition, we address requirements for a standard benchmark model analysis, such as public datasets, common evaluation metrics, and good practices for the match-mismatch task. Significance. We are the first to present a review paper summarizing the main deep-learning-based studies that relate EEG to speech while addressing methodological pitfalls and important considerations for this newly expanding field. Our study is particularly relevant given the growing application of deep learning in EEG-speech decoding