17 research outputs found
Context-Aware Recursive Bayesian Graph Traversal in BCIs
Noninvasive brain computer interfaces (BCI), and more specifically
Electroencephalography (EEG) based systems for intent detection need to
compensate for the low signal to noise ratio of EEG signals. In many
applications, the temporal dependency information from consecutive decisions
and contextual data can be used to provide a prior probability for the upcoming
decision. In this study we proposed two probabilistic graphical models (PGMs),
using context information and previously observed EEG evidences to estimate a
probability distribution over the decision space in graph based decision-making
mechanism. In this approach, user moves a pointer to the desired vertex in the
graph in which each vertex represents an action. To select a vertex, a Select
command, or a proposed probabilistic Selection criterion (PSC) can be used to
automatically detect the user intended vertex. Performance of different PGMs
and Selection criteria combinations are compared over a keyboard based on a
graph layout. Based on the simulation results, probabilistic Selection
criterion along with the probabilistic graphical model provides the highest
performance boost for individuals with pour calibration performance and
achieving the same performance for individuals with high calibration
performance.Comment: This work has been submitted to EMBC 201
Explainable shared control in assistive robotics
Shared control plays a pivotal role in designing assistive robots to complement human capabilities during everyday tasks. However, traditional shared control relies on users forming an accurate mental model of expected robot behaviour. Without this accurate mental image, users may encounter confusion or frustration whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. The Explainable Shared Control paradigm introduced in this thesis attempts to resolve such model misalignment by jointly considering assistance and transparency.
There are two perspectives of transparency to Explainable Shared Control: the human's and the robot's. Augmented reality is presented as an integral component that addresses the human viewpoint by visually unveiling the robot's internal mechanisms. Whilst the robot perspective requires an awareness of human "intent", and so a clustering framework composed of a deep generative model is developed for human intention inference.
Both transparency constructs are implemented atop a real assistive robotic wheelchair and tested with human users. An augmented reality headset is incorporated into the robotic wheelchair and different interface options are evaluated across two user studies to explore their influence on mental model accuracy. Experimental results indicate that this setup facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment. As for human intention inference, the clustering framework is applied to a dataset collected from users operating the robotic wheelchair. Findings from this experiment demonstrate that the learnt clusters are interpretable and meaningful representations of human intent.
This thesis serves as a first step in the interdisciplinary area of Explainable Shared Control. The contributions to shared control, augmented reality and representation learning contained within this thesis are likely to help future research advance the proposed paradigm, and thus bolster the prevalence of assistive robots.Open Acces
Electroencephalography brain computer interface using an asynchronous protocol
A dissertation submitted to the Faculty of Science,
University of the Witwatersrand, in ful llment of the
requirements for the degree of Master of Science. October 31, 2016.Brain Computer Interface (BCI) technology is a promising new channel for communication
between humans and computers, and consequently other humans. This technology has the
potential to form the basis for a paradigm shift in communication for people with disabilities or
neuro-degenerative ailments. The objective of this work is to create an asynchronous BCI that
is based on a commercial-grade electroencephalography (EEG) sensor. The BCI is intended
to allow a user of possibly low income means to issue control signals to a computer by using
modulated cortical activation patterns as a control signal. The user achieves this modulation
by performing a mental task such as imagining waving the left arm until the computer performs
the action intended by the user. In our work, we make use of the Emotiv EPOC headset to
perform the EEG measurements. We validate our models by assessing their performance when
the experimental data is collected using clinical-grade EEG technology. We make use of a
publicly available data-set in the validation phase.
We apply signal processing concepts to extract the power spectrum of each electrode from
the EEG time-series data. In particular, we make use of the fast Fourier transform (FFT).
Specific bands in the power spectra are used to construct a vector that represents an abstract
state the brain is in at that particular moment. The selected bands are motivated by insights
from neuroscience. The state vector is used in conjunction with a model that performs classification. The exact purpose of the model is to associate the input data with an abstract
classification result which can then used to select the appropriate set of instructions to be executed
by the computer. In our work, we make use of probabilistic graphical models to perform
this association.
The performance of two probabilistic graphical models is evaluated in this work. As a
preliminary step, we perform classification on pre-segmented data and we assess the performance
of the hidden conditional random fields (HCRF) model. The pre-segmented data has a trial
structure such that each data le contains the power spectra measurements associated with only
one mental task. The objective of the assessment is to determine how well the HCRF models the
spatio-spectral and temporal relationships in the EEG data when mental tasks are performed
in the aforementioned manner. In other words, the HCRF is to model the internal dynamics
of the data corresponding to the mental task. The performance of the HCRF is assessed over
three and four classes. We find that the HCRF can model the internal structure of the data
corresponding to different mental tasks.
As the final step, we perform classification on continuous data that is not segmented and
assess the performance of the latent dynamic conditional random fields (LDCRF). The LDCRF
is used to perform sequence segmentation and labeling at each time-step so as to allow the
program to determine which action should be taken at that moment. The sequence segmentation
and labeling is the primary capability that we require in order to facilitate an asynchronous
BCI protocol. The continuous data has a trial structure such that each data le contains the
power spectra measurements associated with three different mental tasks. The mental tasks
are randomly selected at 15 second intervals. The objective of the assessment is to determine
how well the LDCRF models the spatio-spectral and temporal relationships in the EEG data,
both within each mental task and in the transitions between mental tasks. The performance of
the LDCRF is assessed over three classes for both the publicly available data and the data we
obtained using the Emotiv EPOC headset. We find that the LDCRF produces a true positive
classification rate of 82.31% averaged over three subjects, on the validation data which is in the
publicly available data. On the data collected using the Emotiv EPOC, we find that the LDCRF
produces a true positive classification rate of 42.55% averaged over two subjects.
In the two assessments involving the LDCRF, the random classification strategy would
produce a true positive classification rate of 33.34%. It is thus clear that our classification
strategy provides above random performance on the two groups of data-sets. We conclude that
our results indicate that creating low-cost EEG based BCI technology holds potential for future
development. However, as discussed in the final chapter, further work on both the software and
low-cost hardware aspects is required in order to improve the performance of the technology as
it relates to the low-cost context.LG201
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
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
MAPiS 2019 - First MAP-i Seminar: proceedings
This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i
Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students,
in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in
Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to
form highly-qualified professionals, fostering their capacity and knowledge to the research area.
This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce
concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD
successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of
organizing and participate in such a complex event. These students were in charge of the organization and management of all the
aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference
and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the
Informatics area. (...)publishe
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Front-Line Physicians' Satisfaction with Information Systems in Hospitals
Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe
Personality Identification from Social Media Using Deep Learning: A Review
Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed