9,209 research outputs found
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is
most commonly based on subjective clinical interpretations. Quantitative
electroencephalography (QEEG) measures have been shown to reflect
neurodegenerative processes in AD and might qualify as affordable and thereby
widely available markers to facilitate the objectivization of AD assessment.
Here, we present a novel framework combining Riemannian tangent space mapping
and elastic net regression for the development of brain atrophy markers. While
most AD QEEG studies are based on small sample sizes and psychological test
scores as outcome measures, here we train and test our models using data of one
of the largest prospective EEG AD trials ever conducted, including MRI
biomarkers of brain atrophy.Comment: Presented at NIPS 2017 Workshop on Machine Learning for Healt
EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers
Several models defining different types of cognitive human behaviour are available. For this work, we
have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model
is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools
able to assess at which level of cognitive control the operator is dealing with the considered task, that
is if he/she is performing the task as an automated routine (skill level), as procedures-based activity
(rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK
behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such
behaviours have been evaluated from a neurophysiological point of view, for example, by considering
brain activity variations across the different SRK levels. Therefore, the proposed study aimed to
investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly
to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers,
demonstrated that specific brain features could characterize and discriminate the different SRK levels,
therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic
setting
Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach
In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available.The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios
Modern GPR Target Recognition Methods
Traditional GPR target recognition methods include pre-processing the data by
removal of noisy signatures, dewowing (high-pass filtering to remove
low-frequency noise), filtering, deconvolution, migration (correction of the
effect of survey geometry), and can rely on the simulation of GPR responses.
The techniques usually suffer from the loss of information, inability to adapt
from prior results, and inefficient performance in the presence of strong
clutter and noise. To address these challenges, several advanced processing
methods have been developed over the past decade to enhance GPR target
recognition. In this chapter, we provide an overview of these modern GPR
processing techniques. In particular, we focus on the following methods:
adaptive receive processing of range profiles depending on the target
environment; adoption of learning-based methods so that the radar utilizes the
results from prior measurements; application of methods that exploit the fact
that the target scene is sparse in some domain or dictionary; application of
advanced classification techniques; and convolutional coding which provides
succinct and representatives features of the targets. We describe each of these
techniques or their combinations through a representative application of
landmine detection.Comment: Book chapter, 56 pages, 17 figures, 12 tables. arXiv admin note:
substantial text overlap with arXiv:1806.0459
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