Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease.

Objective.This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD).Approach.The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert-Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs).Main results.With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD.Significance.Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

A head mounted augmented reality design practice for maintenance assembly: Toward meeting perceptual and cognitive needs of AR users.

Head Mounted Display (HMD) based Augmented Reality (AR) is being increasingly used in manufacturing and maintenance. However, limited research has been done to understand user interaction with AR interfaces, which may lead to poor usability, risk of occupational hazards, and low acceptance of AR systems. This paper uses a theoretically-driven approach to interaction design to investigate the impact of different AR modalities in terms of information mode (i.e. video vs. 3D animation) and interaction modality (i.e. hand-gesture vs. voice command) on user performance, workload, eye gaze behaviours, and usability during a maintenance assembly task. The results show that different information modes have distinct impacts compared to paper-based maintenance, in particular, 3D animation led to a 14% improvement over the video instructions in task completion time. Moreover, insights from eye gaze behaviours such as number of fixations and transition between Areas of Interest (AOIs) revealed the differences in attention switching and task comprehension difficulty with the choice of AR modalities. While, subjective user perceptions highlight some ergonomic issues such as misguidance and overreliance, which must be considered and addressed from the joint cognitive systems' (JCSs) perspective and in line with the predictions derived from the Multiple Resources Model.

Quantifying Uncertainty in Pulsed Thermographic Inspection by Analysing the Thermal Diffusivity Measurements of Metals and Composites.

Pulsed thermography has been used significantly over the years to detect near and sub-surface damage in both metals and composites. Where most of the research has been in either improving the detectability and/or its applicability to specific parts and scenarios, efforts to analyse and establish the level of uncertainty in the measurements have been very limited. This paper presents the analysis of multiple uncertainties associated with thermographic measurements under multiple scenarios such as the choice of post-processing algorithms; multiple flash power settings; and repeat tests on four materials, i.e., aluminium, steel, carbon-fibre reinforced plastics (CFRP) and glass-fibre reinforced plastics (GFRP). Thermal diffusivity measurement has been used as the parameter to determine the uncertainty associated with all the above categories. The results have been computed and represented in the form of a relative standard deviation (RSD) ratio in all cases, where the RSD is the ratio of standard deviation to the mean. The results clearly indicate that the thermal diffusivity measurements show a large RSD due to the post-processing algorithms in the case of steel and a large variability when it comes to assessing the GFRP laminates.

Motion blur invariant for estimating motion parameters of medical ultrasound images.

High-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.