7,181 research outputs found
Deep Learning Techniques for Electroencephalography Analysis
In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective
Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes
Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.</p
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
A view of colonial life in South Australia: An osteological investigation of the health status among 19th-century migrant settlers
Studies of human skeletal remains contribute to understanding the extent to which conditions
prevailing in various past communities were detrimental to health. Few of these studies have
evaluated the situation in which the first European colonists of South Australia lived.
Colonial Australian skeletal collections are scarce, especially for research purposes. This
makes the 19th-century skeletal remains of individuals, excavated from St Mary’s Cemetery,
South Australia, a rare and valuable collection.
The overarching aim of this thesis was to investigate the general and oral health of this
specific group of 19th-century settlers, through the examination of their skeletons and
dentitions. Four research papers in this thesis address this overarching aim. The first two
papers determine the general skeletal health of the settlers, with a focus on pathological
manifestations on bones associated with metabolic deficiencies and the demands of
establishing an industrial society. Paper 3 investigated whether Large Volume Micro-
Computed Tomography (LV Micro-CT) could be used as a single technique for the analysis
of the in situ dentoalveolar complex of individuals from St Mary’s. This led to a detailed
investigation of the dentitions of the St Mary’s sample, in paper 4, with the aims of
determining the oral health status of these individuals, and understanding how oral conditions
may have influenced their general health.
The skeletal remains of 65 individuals (20 adults and 45 subadults) from St Mary’s sample
were available for the four component investigations using non-destructive techniques -
macroscopic, radiographic and micro-CT methods.
Signs of nutritional deficiencies (vitamin C and iron) were identified in Paper 1. The findings
of paper 2 showed joint diseases and traumatic fractures were seen and that gastrointestinal and pulmonary conditions were the leading causes of death in subadults and adults
respectively. Paper 3 found that the LV Micro-CT technique was the only method able to
generate images that allowed the full range of detailed measurements across all the oral
health categories studied. A combination of macroscopic and radiographic techniques
covered a number of these categories, but was more time-consuming, and did not provide the
same level of accuracy or include all measurements. Results for paper 4 confirmed that
extensive carious lesions, antemortem tooth loss and evidence of periodontal disease were
present in the St Mary’s sample. Developmental defects of enamel (EH) and areas of
interglobular dentine (IGD) were identified. Many individuals with dental defects also had
skeletal signs of co-morbidities. St Mary’s individuals had a similar percentage of carious
lesions as the British sample, which was more than other historic Australian samples, but less
than a contemporary New Zealand sample.
The 19th-century migrants to the colony of South Australia were faced with multiple
challenges such as adapting to local environmental conditions as well as participating in the
development of settlements, infrastructure and new industries. Evidence of joint diseases,
traumatic injuries and health insults, seen as pathological changes and/ or abnormalities on
the bone and/or teeth, confirmed that the settlers' health had been affected. The number of
burials in the ‘free ground’ area between the 1840s -1870s was greater than the number in the
leased plots, reflecting the economic problems of the colony during these early years.
Validation of the reliability and accuracy of the LV Micro-CT system for the analysis of the
dentoalveolar complex, in situ within archaeological human skull samples, provided a
microanalytical approach for the in-depth investigations of the St Mary’s dentition. Extensive
carious lesions, antemortem tooth loss and periodontal disease seen in this group would have
affected their general health status. The presence of developmental defects (EH and IGD)
indicated that many of the settlers had suffered health insults in childhood to young adulthood. Contemporaneous Australian, New Zealand and British samples had comparable
findings suggesting that little improvement had occurred in their oral health since arriving in
South Australia.
In conclusion, the findings of this investigation largely fulfilled the initial aims. Our
understanding of the extent to which conditions prevailing in the new colony were
detrimental to human health has increased, as has our knowledge of why pathological
manifestations and/or abnormalities were seen on the bones and teeth of individuals from the
St Mary’s sample. A multiple-method approach, to derive enhanced information has been
shown to be effective, whilst establishing a new methodology (LV Micro-CT) for the analysis
of dentition in situ in human archaeological skulls. Further, this investigation has digitally
preserved data relating to this historical group of individuals for future comparisons.Thesis (Ph.D.) -- University of Adelaide, School of Biomedicine, 202
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
When smaller is more – investigating the interplay between continuous sensory cues and numerical information
Research on numerical cognition is not limited to symbolic numbers and mathematics but it also includes discrete and continuous magnitudes. Continuous magnitudes are ubiquitous in nature and serve as important cues in everyday life situations. When one tries to choose the plate with more cookies in the cafeteria, they usually do not count the cookies but rather arrive at a fair estimate by comparing such continuous magnitudes. For example, nine cookies on a plate will occupy a larger area and have to be placed denser to each other than five cookies. Recent research has shown that, as opposed to the classical view, the processing of symbolic numbers and non-symbolic numerosities is not independent from such sensory cues. The present dissertation consists of two studies that investigate what psychological processes underlie the interaction between sensory cues and numerical information.
Study 1 aimed to replicate and extend the findings of Gebuis & Reynvoet who systematically manipulated the relationship between continuous and discrete magnitudes in a non-symbolic numerical comparison task. The main goal was to assess the stability and the robustness of the influence of sensory cues on numerical comparisons as the originally reported patterns suggest a complex interaction between these two kinds of information that are difficult to reconcile with the classic views on numerical processing. Indeed, the results confirmed that continuous magnitudes have a complex effect on numerical judgements and that their interaction can be either due to incomplete inhibition or due to integration of continuous magnitudes during numerical tasks.
Study 2 turned to symbolic numbers and investigated whether inhibition underlies the interaction of continuous sensory properties and numerical information. To this end a novel paradigm was introduced that allowed to investigate well-established electrophysiological correlates of inhibition with numerical stimuli. The results provide evidence that inhibition underlies the interaction between sensory cues and numerical information. Additionally, they show that the paradigm introduced in Study 2 may suitable to investigate these processes across different developmental stages and numeracy levels
The Omics basis of human health: investigating plasma proteins and their genetic effects on complex traits
Over the past decade, the advancements in technology and the growing amount of identified genetic variants have led to a high number of important discoveries in the field of precision medicine concerning human biology and pathophysiology. However, it became evident that genomics alone could not properly explain the onset and regulation of the specific molecular mechanisms of certain phenotypes. Studying omics helped complement this gap in genetic research, providing detailed information on the quantification of molecules that are involved in structural and functional processes in the organism. Specifically, protein production, levels, and regulation are dynamic and change during the course of one’s lifetime. This information has proven fundamental to understanding how certain proteins affect complex phenotypes such as neurological and psychiatric disorders.
In this thesis, I describe the three groups of analyses I conducted over the course of my doctoral programme on different sets of blood plasma proteins and over a broad range of neurological, psychiatric, cardiovascular, and electrophysiology phenotypes. The underlying mechanisms that trigger the onset of psychiatric and neurological conditions are often not limited to the nervous system, but rather stem from multi-system molecular triggers. The first part of the work I carried out aims at investigating the frequent co-occurrence and comorbidity of neurological and cardiovascular phenotypes by conducting a genome-wide association (GWA) meta-analysis of 183 neurology-related blood proteins on data from over 12000 individuals. The second part concerns the bivariate and multivariate analyses conducted on 276 cardiology and inflammatory proteins, while the third illustrates the contribution to consortia focussed on heart rate and electrophysiology. Results from the second and third parts of the work provided information that played an important role in understanding a part of the genetic mechanisms of the complex traits of interest.
Overall, the results presented in this thesis strongly support the notion
that proteomics is an important tool to be used to study complex traits and drug discovery and development should focus on targeting protein synthesis and regulation. Furthermore, the results also support the notion that complex diseases involve more than one biological system, and in order to gain a better understanding of human pathology, it is fundamental to study the causes and effects across the entire organism
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
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