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Existing methods for assessing long-term memory (LTM) rely predominantly on psychometric tests or clinical expert observations. In this study, we propose an objective method for evaluating semantic LTM ability using resting-state electroencephalography (EEG) functional connectivity. Data from 68 participants were analysed, deriving functional connectivity from the phase information of EEG theta (4–8 Hz), alpha (8–13 Hz) and gamma (30–45 Hz) frequency bands across the entire scalp at resting state. Participants' responses were recorded during a memory recall task over four sessions. Multiple linear regression was used to model the LTM score. The proposed method successfully predicted LTM retention after 30 min, with performance metrics of F (18,49) = 2.216, p = 0.014, R =0.670; 2 months retention, F (18,45) = 3.057, p < 0.001, R =0.742; 4 months retention, F (18,42) = 2.237, p = 0.016, R =0.700; and 6 months retention, F (18,36) = 1.988, p = 0.039, R =0.706, respectively. Additionally, this method achieved at least 27 points lower in the Bayesian Information Criterion (BIC) compared to the standard psychometric RAPM test across all retention periods. These findings suggest that the semantic LTM ability of healthy young individuals can be objectively quantified using resting-state EEG functional connectivity. This approach holds promise for future applications in understanding and addressing below standard performance in students learning. [ABSTRACT FROM AUTHOR]

Abstract The early detection of mental stress is critical for efficient clinical treatment. As compared with traditional approaches, the automatic methods presented in literature have shown significance and effectiveness in terms of diagnosis speed. Unfortunately, the majority of them mainly focus on accuracy rather than predictions for treatment efficacy. This may result in the development of methods that are less robust and accurate, which is unsuitable for clinical purposes. In this study, we propose a comprehensive framework for the early detection of mental stress by analysing variations in both electroencephalogram (EEG) and electrocardiogram (ECG) signals from 22 male subjects (mean age: 22.54 ± 1.53 years). The significant contribution of this paper is that the presented framework is capable of performing predictions for treatment efficacy, which is achieved by defining four stress levels and creating models for the individual level. The experimental results indicate that the framework has realised an accuracy, a sensitivity, and a specificity of 79.54%, 81%, and 78%, respectively. Moreover, the results indicate significant neurophysiological differences between the stress and control (stress-free) conditions at the individual level. [ABSTRACT FROM AUTHOR]

The discrimination of four simple limb motor imagery movements for brain-computer interface (BCI) applications is still challenging. This is because most of the movement imaginations have close spatial representations on the motor cortex area. Nevertheless, due to its potential applications in significant areas including BCI, solutions need to be formulated to overcome the task discrimination issues faced when a motor imagery movement approach is utilized. Feature extraction is one of the most important steps in any BCI system; as such, enhancement to the existing methods has been incorporated in this work. For this, we propose four-class movement imaginations of the right hand, left hand, right foot, and left foot, and develop feature extraction methods utilizing discrete wavelet transform (DWT) and empirical mode decomposition (EMD); in both methods, artificial neural network (ANN) was used as a classifier. Based on the processed electroencephalography (EEG) data recorded from eleven subjects, it can be seen that EMD features outperform DWT features; the average accuracy achieved by the EMD features is 90.02%, and 84.77% using the DWT features. EMD even performs better than DWT in discriminating the most challenging tasks involving the right foot and left foot imageries, whose EEG data were derived from the same Cz node of the motor cortex. [ABSTRACT FROM AUTHOR]

Recently, Electroencephalogram (EEG)-based computer-aided (CAD) techniques have shown their promise as decision-making tools to diagnose major depressive disorder (MDD) or simply depression. Although the research results have motivated the use of CAD techniques to help assist psychiatrists in clinics yet their clinical translation has been less clear and remains a research topic. In this paper, a proposed machine learning (ML) scheme was tested and validated with resting-state EEG data involving 33 MDD patients and 30 healthy controls. The EEG-derived measures such as power of different EEG frequency bands and EEG alpha interhemispheric asymmetry were investigated as input features to the proposed ML scheme to discriminate the MDD patients and healthy controls, and to prove their feasibility for diagnosing depression. The acquired EEG data were subjected to noise removal and feature extraction. As a result, a data matrix was constructed by the columns-wise concatenation of the extracted features. Furthermore, the z-score standardization was performed to standardize each column of the data matrix according to its mean and variance. The data matrix may have redundant and irrelevant features; therefore, to determine the most significant features, a weight was assigned to each feature based on its ability to separate the target classes according to the criterion, i.e., receiver operating characteristics (roc). Hence, only the most significant features were used for testing and training the classifier models: Logistic regression (LR), Support vector machine (SVM), and Naïve Bayesian (NB). Finally, the classifier models were validated with 10-fold cross-validation that has provided the performance metrics such as test accuracy, sensitivity, and specificity. As a result of the investigations, most significant features such as EEG signal power and EEG alpha interhemispheric asymmetry from the brain areas such as frontal, temporal, parietal and occipital were found significant. In addition, the proposed ML framework proved automatic identification of aberrant EEG patterns specific to disease conditions and provide high classification results i.e., LR classifier (accuracy = 97.6%, sensitivity = 96.66%, specificity = 98.5%), NB classification (accuracy = 96.8%, sensitivity = 96.6%, specificity = 97.02%), and SVM (accuracy = 98.4%, sensitivity = 96.66%, specificity = 100%). In conclusion, the proposed ML scheme along with the EEG signal power and EEG alpha interhemispheric asymmetry are proved suitable as clinical diagnostic tools for MDD. [ABSTRACT FROM AUTHOR]

The selection of suitable antidepressants for Major Depressive Disorder (MDD) has been challenging and is mainly based on subjective assessments that include minimal scientific evidence. Objective measures that are extracted from neuroimaging modalities such as electroencephalograms (EEGs) could be a potential solution to this problem. This approach is achieved by the successful prediction of antidepressant treatment efficacy early in the patient's care. EEG-based relevant research studies have shown promising results. These studies are based on derived measures from EEG and event-related potentials (ERPs), which are called neurophysiological predictive biomarkers for MDD. This paper seeks to provide a detailed review on such research studies along with their possible limitations. In addition, this paper provides a comparison of these methods based on EEG/ERP common datasets from MDD and healthy controls. This paper also proposes recommendations to improve these methods, e.g., EEG integration with other modalities such as functional magnetic resonance imaging (fMRI) and magnetoencephalograms (MEG), to achieve better evidence of the efficacy than EEG alone, to eventually improve the treatment selection process. [ABSTRACT FROM AUTHOR]

Rationale and Objectives Quantitative assessment of knee articular cartilage (AC) morphology using magnetic resonance (MR) imaging requires an accurate segmentation and 3D reconstruction. However, automatic AC segmentation and 3D reconstruction from hydrogen-based MR images alone is challenging because of inhomogeneous intensities, shape irregularity, and low contrast existing in the cartilage region. Thus, the objective of this research was to provide an insight into morphologic assessment of AC using multilevel data processing of multinuclear ( 23 Na and 1 H) MR knee images. Materials and Methods A dual-tuned ( 23 Na and 1 H) radio-frequency coil with 1.5-T MR scanner is used to scan four human subjects using two separate MR pulse sequences for the respective sodium and proton imaging of the knee. Postprocessing is performed using customized routines written in MATLAB. MR data were fused to improve contrast of the cartilage region that is further used for automatic segmentation. Marching cubes algorithm is applied on the segmented AC slices for 3D volume rendering and volume is then calculated using the divergence theorem. Results Fusion of multinuclear MR images results in an improved contrast (factor >3) in the cartilage region. Sensitivity (80.21%) and specificity (99.64%) analysis performed by comparing manually segmented AC shows a good performance of the automated AC segmentation. The average cartilage volume (23.19 ± 1.38 cm 3 ; coefficient of variation [COV] −0.059) measured from 3D AC models of four data sets shows a marked improvement over average cartilage volume (23.24 cm 3 ; COV −0.19) reported earlier. Conclusions This study confirms the use of multinuclear MR data for cartilage morphology (volume) assessment that can be used in clinical settings. [ABSTRACT FROM AUTHOR]

Abstract: The EEG source localization which is used to localize the electrical activity of brain has been an active area of research as it provides useful information for study of brain's physiological, mental and functional abnormalities. This problem is called EEG inverse problem. The localization of the active sources needs the solution of ill posed EEG inverse problem. Since the foundation of this field till today, many methods have been developed with the aim of in-depth localization, high resolution, reduction in localization/energy error and decreased computational time. In this survey, EEG inverse problem is discussed with its primary to most developed and recent solutions. The introduction to the field along with the categorization of different solutions is provided. Also, the relative advantages and limitations for each method are discussed. Finally, the challenges and future recommendations are provided, in the end, for further improvement of EEG inverse problem in terms of resolution, computational power and localization error. [Copyright &y& Elsevier]

Abstract: With increasing blackouts owing to vegetation encroachments for transmission lines right-of-ways, it has become imperative for electric utilities to review their vegetation management practices to avoid incidents of un-intended encroachments. In this paper, advantages and limitations of existing techniques for inspecting transmission lines is presented. Regarding the clearance of un-intended vegetation for transmission lines right-of-ways, the surveillance of transmission lines is performed periodically through visual inspection, or by airborne system. The geographical information system (GIS) containing the geo-referenced data of assets, lands, wherefrom the transmission lines pass are essential tools for the improvement of transmission lines maintenance. Air-borne LiDAR scanners, videography, and aerophotogranometry are now available for surveillance applications. These tools, because of their accuracy in spatial resolution, can be applied to track not only invasions, but also monitor the vegetation surrounding the transmission lines right-of-ways. The paper discusses concept of utilizing multispectral satellite stereo images to recover 3D-digital elevation model (DEM) of transmission lines right-of-ways to identify dangerous vegetation that can strike the power lines to cause blackouts. Further, a new wireless multimedia sensor networks (WMSNs) based method is proposed which is cost effective, less time consuming and more accurate for the automated power line inspection against vegetation encroachments. [Copyright &y& Elsevier]

Abstract: In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates system''s dynamics with Gaussian model while particle filter approximates system''s dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle. [Copyright &y& Elsevier]

Abstract: Three-dimensional shape recovery from one or multiple observations is a challenging problem of computer vision. In this paper, we present a new Focus Measure for the estimation of a depth map using image focus. This depth map can subsequently be used in techniques and algorithms leading to the recovery of a three-dimensional structure of the object, a requirement of a number of high level vision applications. The proposed Focus Measure has shown robustness in the presence of noise as compared to the earlier Focus Measures. This new Focus Measure is based on an optical transfer function implemented in the Fourier domain. The results of the proposed Focus Measure have shown drastic improvements in estimation of a depth map, with respect to the earlier Focus Measures, in the presence of various types of noise including Gaussian, Shot, and Speckle noises. The results of a range of Focus Measures are compared using root mean square error and correlation metric measures. [Copyright &y& Elsevier]

Abstract: Obtaining an accurate and precise depth map is the ultimate goal for 3D shape recovery. For depth map estimation, one of the most vital parts is the initial selection of the focus measure and processing the images with the selected focus measure. Although, many focus measures have been proposed in the literature but not much attention has been paid to the factors affecting those focus measures as well as the manner the images are processed with those focus measures. In this paper, for accurate calculation of depth map, we consider the effects of illumination on the depth map as well as the selection of the window size for application of the focus measures. The resulting depth map can further be used in techniques and algorithms leading to recovery of three-dimensional structure of the object which is required in many high-level vision applications. It is shown that the illumination effects can directly result in incorrect estimation of depth map if proper window size is not selected during focus measure computation. Further, it is shown that the images need some kind of pre-processing to enhance the dark regions and shadows in the image. For this purpose, an adaptive enhancement algorithm is proposed for pre-processing. In this paper, we prove that without such pre-processing for image enhancement and without the use of proper window size for the estimation of depth maps, it is not possible to obtain the accurate depth map. [Copyright &y& Elsevier]

• Fusion of CNN and sparse representation for disparity map estimation using UAV and satellite stereo images. • Extracted different feature points from stereo images to measures the accurate depth of objects near power lines and poles. • The height of power poles and trees are estimated based on the computed disparity maps. • Compared state of the art global and local matching methods for disparity map estimation. Fires or electrical hazards and accidents can occur if vegetation is not controlled or cleared around overhead power lines, resulting in serious risks to people and property and significant costs to the community. There are numerous blackouts due to interfering the trees with the power transmission lines in hilly and urban areas. Power distribution companies are facing a challenge to monitor the vegetation to avoid blackouts and flash-over threats. Recently, several methods have been developed for vegetation monitoring; however, existing methods are either not accurate or could not provide better disparity map in the textureless region. Moreover, are not able to handle depth discontinuity in stereo thus are not able to find a feasible solution in the smooth areas to compute the disparity map. This study presents a cost-effective framework based on UAV and satellite Stereo images to monitor the trees and vegetation, which provide better disparity. We present a novel approach based on the fusion of the convolutional neural network (CNN) and sparse representation that handled textureless region, depth discontinuity and smooth region to produce better disparity map that further used for threat estimation using height and distance of vegetation/trees near power lines and poles. Extensive experimental evaluation on real time powerline monitoring showed considerable imporvemnt in vegetation threat estimation with accuracy of 90.3% in comparison to graph-cut, dynamic programming, belief propagation, and area-based methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a framework based on a single dual-purpose semi-fragile watermark to verify the integrity of digital image along with the recovery of distorted image. The watermark is correlated to the host image for detecting the collage attack and then embedded in their respective wavelet subbands. Unlike the conventional block-based approaches, this work has the ability to determine the unverified regions concisely. Huffman and BCH coding are utilized while generating the watermark. Integer DCT has been exploited as it can be highly compressed by Huffman coding as compared to the conventional DCT contents. The proposed technique exhibits the flexibility between imperceptibility, robustness, and capacity. In addition, integer wavelet transform has been used to reduce the computational complexity of the algorithm. Evaluation of experimental investigation shows the performance of dual-purpose semi-fragile watermark. [Copyright &y& Elsevier]

Abstract: Osteoarthritis is a common joint disorder that is most prevalent in the knee joint. Knee osteoarthritis (OA) can be characterized by the gradual loss of articular cartilage (AC). Formation of lesion, fissures and cracks on the cartilage surface has been associated with degenerative AC and can be measured by morphological assessment. In addition, loss of proteoglycan from extracellular matrix of the AC can be measured at early stage of cartilage degradation by physiological assessment. In this case, a biochemical phenomenon of cartilage is used to assess the changes at early degeneration of AC. In this paper, a method to measure local sodium concentration in AC due to proteoglycan has been investigated. A clinical 1.5-T magnetic resonance imaging (MRI) with multinuclear spectroscopic facility is used to acquire sodium images and quantify local sodium content of AC. An optimised 3D gradient-echo sequence with low echo time has been used for MR scan. The estimated sodium concentration in AC region from four different data sets is found to be ~225±19mmol/l, which matches the values that has been reported for the normal AC. This study shows that sodium images acquired at clinical 1.5-T MRI system can generate an adequate quantitative data that enable the estimation of sodium concentration in AC. We conclude that this method is potentially suitable for non-invasive physiological (sodium content) measurement of articular cartilage. [Copyright &y& Elsevier]

Our research employed the EEG to examine the effects of different cognitive tasks (math and decision making problems) on drivers’ cognitive state. Forty-two subjects participated in this study. Two simulated driving sessions, driving with distraction task and driving only, were designed to investigate the impact of a secondary task on EEG responses as well as the driving performance. We found that engaging the driver’s cognitively with a secondary task significantly affected his/her driving performance as well as the judgment capability. Moreover, we found that different features of the secondary task had different effects on EEG responses and different localizations in the frontal cortex. Our hemispheric analysis results showed that the most affected area during distracted driving was in the right frontal cortex region; thus, it is suggested that the activation in the right frontal cortex region may be considered the spatial index that indicated a driver who is in a state of cognitive distraction. [ABSTRACT FROM AUTHOR]

Stereoscopic 3D (S3D) displays provide a very realistic visualization experience to the viewers through a sensation of depth perception. Despite the rapid growth of S3D technology (S3D) in education, very little has been known about the effects of S3D on human behavior and the corresponding brain's responses over traditional 2D display during conscious learning and Long-Term Memory (LTM) recall process. The present study aims to explore the effects of S3D technology on human behavior and the brain responses during learning and memory using an electroencephalography (EEG) technique. A sample of 68 participants between 18 and 30 years of age was recruited to perform three tasks: (1) Raven's Advanced Progressive Matrices (RAPM) test to assess the fluid intelligence ability; (2) learning task to study the S3D or 2D based contents for learning; and (3) memory recall task to assess the memory recall and retention performance. The participants were randomly assigned to two groups―2D group and S3D group, in such a way that their fluid intelligence ability, age, and background knowledge about the learning material are controlled. The analysis of behavioral data suggested that responses of both groups in terms of reaction time after two months of retention were statistically significantly different, F (2.66,125.09) = 4.47, p =.007, η p 2 = 0.087. The differences between the S3D and 2D groups were identified by EEG analysis, i.e., the classifier correctly discriminated the EEG signals of both groups with more than 90% accuracy rate. The EEG source analysis showed statistically significant differences between the groups in the brain regions of BA 7, BA 10, BA 11, and BA 25, reflecting widespread neuronal networks involved in the S3D group as compared to the 2D group in the LTM recollection. In conclusion, it was experimentally showed that the human brain processed the S3D contents differently by utilizing more cortical regions and neuronal networks than the traditional 2D contents, which modulates the behavior of the participants by recollecting the LTM faster in the recall task. • Investigated the impacts of Stereoscopic 3D (S3D) Technology on human behavior during learning and Long-Term Memory (LTM). • EEG signals with behavioral responses collected from sixty-eight human participants during learning and memory experiment. • Extracted EEG features using wavelet transform with arithmetic coding algorithm and classified with Machine Learning. • Data showed that the human brain processed the S3D contents with widespread cortical networks as compared to 2D contents. • The use of S3D technology modulated the cortical activities, resulting in faster retrieval of LTM by the participants. [ABSTRACT FROM AUTHOR]