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Numerical investigation of treated brain glioma model using a two-stage successive over-relaxation method

Hussain A, Muthuvalu MS, Faye I, Zafar M, Inc M, Afzal F, et al.

Comput Biol Med, 2023 Feb;153:106429.
PMID: 36587570 DOI: 10.1016/j.compbiomed.2022.106429

A brain tumor is a dynamic system in which cells develop rapidly and abnormally, as is the case with most cancers. Cancer develops in the brain or inside the skull when aberrant and odd cells proliferate in the brain. By depriving the healthy cells of leisure, nutrition, and oxygen, these aberrant cells eventually cause the healthy cells to perish. This article investigated the development of glioma cells in treating brain tumors. Mathematically, reaction-diffusion models have been developed for brain glioma growth to quantify the diffusion and proliferation of the tumor cells within brain tissues. This study presents the formulation the two-stage successive over-relaxation (TSSOR) algorithm based on the finite difference approximation for solving the treated brain glioma model to predict glioma cells in treating the brain tumor. Also, the performance of TSSOR method is compared to the Gauss-Seidel (GS) and two-stage Gauss-Seidel (TSGS) methods in terms of the number of iterations, the amount of time it takes to process the data, and the rate at which glioma cells grow the fastest. The implementation of the TSSOR, TSGS, and GS methods predicts the growth of tumor cells under the treatment protocol. The results show that the number of glioma cells decreased initially and then increased gradually by the next day. The computational complexity analysis is also used and concludes that the TSSOR method is faster compared to the TSGS and GS methods. According to the results of the treated glioma development model, the TSSOR approach reduced the number of iterations by between 8.0 and 71.95%. In terms of computational time, the TSSOR approach is around 1.18-76.34% faster than the TSGS and GS methods.
A novel method for efficient estimation of brain effective connectivity in EEG

Khan DM, Yahya N, Kamel N, Faye I

Comput Methods Programs Biomed, 2023 Jan;228:107242.
PMID: 36423484 DOI: 10.1016/j.cmpb.2022.107242

BACKGROUND AND OBJECTIVE: Brain connectivity plays a pivotal role in understanding the brain's information processing functions by providing various details including magnitude, direction, and temporal dynamics of inter-neuron connections. While the connectivity may be classified as structural, functional and causal, a complete in-vivo directional analysis is guaranteed by the latter and is referred to as Effective Connectivity (EC). Two most widely used EC techniques are Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) which are based on multivariate autoregressive models. The drawbacks of these techniques include poor frequency resolution and the requirement for experimental approach to determine signal normalization and thresholding techniques in identifying significant connectivities between multivariate sources.
METHODS: In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source.
RESULTS: The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively.
CONCLUSION: Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.
Automated tibiofemoral joint segmentation based on deeply supervised 2D-3D ensemble U-Net: Data from the Osteoarthritis Initiative

Latif MHA, Faye I

Artif Intell Med, 2021 Dec;122:102213.
PMID: 34823835 DOI: 10.1016/j.artmed.2021.102213

Improving longevity is one of the greatest achievements in humanity. Because of this, the population is growing older, and the ubiquity of knee osteoarthritis (OA) is on the rise. Nonetheless, the understanding and ability to investigate potential precursors of knee OA have been impeded by time-consuming and laborious manual delineation processes which are prone to poor reproducibility. A method for automatic segmentation of the tibiofemoral joint using magnetic resonance imaging (MRI) is presented in this work. The proposed method utilizes a deeply supervised 2D-3D ensemble U-Net, which consists of foreground class oversampling, deep supervision loss branches, and Gaussian weighted softmax score aggregation. It was designed, optimized, and tested on 507 3D double echo steady-state (DESS) MR volumes using a two-fold cross-validation approach. A state-of-the-art segmentation accuracy measured as Dice similarity coefficient (DSC) for the femur bone (98.6 ± 0.27%), tibia bone (98.8 ± 0.31%), femoral cartilage (90.3 ± 2.89%), and tibial cartilage (86.7 ± 4.07%) is achieved. Notably, the proposed method yields sub-voxel accuracy for an average symmetric surface distance (ASD) less than 0.36 mm. The model performance is not affected by the severity of radiographic osteoarthritis (rOA) grades or the presence of pathophysiological changes. The proposed method offers an accurate segmentation with high time efficiency (~62 s) per 3D volume, which is well suited for efficient processing and analysis of the large prospective cohorts of the Osteoarthritis Initiative (OAI).
Fulltext Conversational Task Increases Heart Rate Variability of Individuals Susceptible to Perceived Social Isolation

Feng YX, Roslan NS, Izhar LI, Abdul Rahman M, Faye I, Ho ETW

Int J Environ Res Public Health, 2021 09 18;18(18).
PMID: 34574777 DOI: 10.3390/ijerph18189858

Studies showed that introversion is the strongest personality trait related to perceived social isolation (loneliness), which can predict various complications beyond objective isolation such as living alone. Lonely individuals are more likely to resort to social media for instantaneous comfort, but it is not a perpetual solution. Largely negative implications including poorer interpersonal relationship and depression were reported due to excessive social media usage. Conversational task is an established intervention to improve verbal communication, cognitive and behavioral adaptation among lonely individuals. Despite that behavioral benefits have been reported, it is unclear if they are accompanied by objective benefits underlying physiological changes. Here, we investigate the physiological signals from 28 healthy individuals during a conversational task. Participants were ranked by trait extraversion, where greater introversion is associated with increased susceptibility to perceived social isolation as compared to participants with greater extraversion as controls. We found that introverts had a greater tendency to be neurotic, and these participants also exhibited significant differences in task-related electrodermal activity (EDA), heart rate (HR) and HR variability (HRV) as compared to controls. Notably, resting state HRV among individuals susceptible to perceived loneliness was below the healthy thresholds established in literature. Conversational task with a stranger significantly increased HRV among individuals susceptible to isolation up to levels as seen in controls. Since HRV is also elevated by physical exercise and administration of oxytocin hormone (one form of therapy for behavioral isolation), conversational therapy among introverts could potentially confer physiological benefits to ameliorate social isolation and loneliness. Our findings also suggest that although the recent pandemic has changed how people are interacting typically, we should maintain a healthy dose of social interaction innovatively.
Fulltext Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network

Al-Hiyali MI, Yahya N, Faye I, Hussein AF

Sensors (Basel), 2021 Aug 04;21(16).
PMID: 34450699 DOI: 10.3390/s21165256

The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger's disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
Fulltext Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study

Al-Ezzi A, Kamel N, Faye I, Gunaseli E

Sensors (Basel), 2021 Jun 15;21(12).
PMID: 34203578 DOI: 10.3390/s21124098

Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1-3 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (13-21 Hz), and high beta (22-30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = -0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.
Fulltext A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students' Interest in the Mathematics Classroom

Babiker A, Faye I

Comput Intell Neurosci, 2021;2021:6617462.
PMID: 33564299 DOI: 10.1155/2021/6617462

Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted k-nearest neighbours (wkNN). The high classification accuracy of 85.7% with the sensitivity of 81.8% and specificity of 90% revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing.
Fulltext Is Response to Genotoxic Stress Similar in Populations of African and European Ancestry? A Study of Dose-Response After in vitro Irradiation

Soumboundou M, Dossou J, Kalaga Y, Nkengurutse I, Faye I, Guingani A, et al.

Front Genet, 2021;12:657999.
PMID: 34868192 DOI: 10.3389/fgene.2021.657999

Background: Exposure to genotoxic stress such as radiation is an important public health issue affecting a large population. The necessity of analyzing cytogenetic effects of such exposure is related to the need to estimate the associated risk. Cytogenetic biological dosimetry is based on the relationship between the absorbed dose and the frequency of scored chromosomal aberrations. The influence of confounding factors on radiation response is a topical issue. The role of ethnicity is unclear. Here, we compared the dose-response curves obtained after irradiation of circulating lymphocytes from healthy donors of African and European ancestry. Materials and Methods: Blood samples from six Africans living in Africa, five Africans living in Europe, and five Caucasians living in Europe were exposed to various doses (0-4 Gy) of X-rays at a dose-rate of 0.1 Gy/min using an X-RAD320 irradiator. A validated cohort composed of 14 healthy Africans living in three African countries was included and blood samples were irradiated using the same protocols. Blood lymphocytes were cultured for 48 h and chromosomal aberrations scored during the first mitosis by telomere and centromere staining. The distribution of dicentric chromosomes was determined and the Kruskal-Wallis test was used to compare the dose-response curves of the two populations. Results: No spontaneous dicentric chromosomes were detected in African donors, thus establishing a very low background of unstable chromosomal aberrations relative to the European population. There was a significant difference in the dose response curves between native African and European donors. At 4 Gy, African donors showed a significantly lower frequency of dicentric chromosomes (p = 8.65 10-17), centric rings (p = 4.0310-14), and resulting double-strand-breaks (DSB) (p = 1.32 10-18) than European donors. In addition, a significant difference was found between African donors living in Europe and Africans living in Africa. Conclusion: This is the first study to demonstrate the important role of ethnic and environmental factors that may epigenetically influence the response to irradiation. It will be necessary to establish country-of-origen-specific dose response curves to practice precise and adequate biological dosimetry. This work opens new perspective for the comparison of treatments based on genotoxic agents, such as irradiation.
Fulltext Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

Awais M, Ghayvat H, Krishnan Pandarathodiyil A, Nabillah Ghani WM, Ramanathan A, Pandya S, et al.

Sensors (Basel), 2020 Oct 12;20(20).
PMID: 33053886 DOI: 10.3390/s20205780

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche-Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
Fulltext Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans

Alsaih K, Yusoff MZ, Tang TB, Faye I, Mériaudeau F

Comput Methods Programs Biomed, 2020 Oct;195:105566.
PMID: 32504911 DOI: 10.1016/j.cmpb.2020.105566

BACKGROUND AND OBJECTIVES: Aged people usually are more to be diagnosed with retinal diseases in developed countries. Retinal capillaries leakage into the retina swells and causes an acute vision loss, which is called age-related macular degeneration (AMD). The disease can not be adequately diagnosed solely using fundus images as depth information is not available. The variations in retina volume assist in monitoring ophthalmological abnormalities. Therefore, high-fidelity AMD segmentation in optical coherence tomography (OCT) imaging modality has raised the attention of researchers as well as those of the medical doctors. Many methods across the years encompassing machine learning approaches and convolutional neural networks (CNN) strategies have been proposed for object detection and image segmentation.
METHODS: In this paper, we analyze four wide-spread deep learning models designed for the segmentation of three retinal fluids outputting dense predictions in the RETOUCH challenge data. We aim to demonstrate how a patch-based approach could push the performance for each method. Besides, we also evaluate the methods using the OPTIMA challenge dataset for generalizing network performance. The analysis is driven into two sections: the comparison between the four approaches and the significance of patching the images.
RESULTS: The performance of networks trained on the RETOUCH dataset is higher than human performance. The analysis further generalized the performance of the best network obtained by fine-tuning it and achieved a mean Dice similarity coefficient (DSC) of 0.85. Out of the three types of fluids, intraretinal fluid (IRF) is more recognized, and the highest DSC value of 0.922 is achieved using Spectralis dataset. Additionally, the highest average DSC score is 0.84, which is achieved by PaDeeplabv3+ model using Cirrus dataset.
CONCLUSIONS: The proposed method segments the three fluids in the retina with high DSC value. Fine-tuning the networks trained on the RETOUCH dataset makes the network perform better and faster than training from scratch. Enriching the networks with inputting a variety of shapes by extracting patches helped to segment the fluids better than using a full image.
Fulltext Longitudinal effects of common carotid artery stenosis on ocular hemodynamics assessed using laser speckle flowgraphy in a rabbit model

Ismail A, Chen HC, Faye I, Tang TB

Sci Rep, 2020 09 28;10(1):15829.
PMID: 32985560 DOI: 10.1038/s41598-020-72556-9

Real-time impairment of ocular blood flow (OBF) under common carotid artery stenosis (CCAS) has not been ascertained. We aimed to longitudinally assess the impact of CCAS on OBF using a rabbit model. About 75% stenosis was created by tying the common carotid artery with a plastic mandrel using a nylon suture. The plastic mandrel was gently removed, leaving a ligature. Neurological and behavioral assessments were recorded as the clinical indicator of stroke severity. With laser speckle flowgraphy, the pulse waveform parameters namely mean blur rate (MBR), blowout score (BOS), blowout time (BOT), rising rate, S1-area, falling rate (FR), S2-area, flow acceleration index (FAI), acceleration time index, resistive index (RI) and the difference between the maximum and minimum values of MBR (AC) were assessed in overall, vessel, and tissue regions of the optic nerve head (ONH). Longitudinally, BOS significantly increased until day 19 post-surgery, whereas FAI, RI, and AC significantly decreased. Beyond day 19, BOS, BOT, FR, FAI, RI, and AC significantly decreased. We defined two stages representing impaired vessel conditions, namely the vessel resistance phase, where BOS increases and FAI, RI, and AC decrease, and the vessel elasticity phase where BOS, BOT, FR, FAI, RI and AC decrease. These stages provide information about atherosclerosis, assessable non-invasively through the eye.
Fulltext Co-creation or Co-destruction: A Perspective of Online Customer Engagement Valence

Siddique J, Shamim A, Nawaz M, Faye I, Rehman M

Front Psychol, 2020;11:591753.
PMID: 33613353 DOI: 10.3389/fpsyg.2020.591753

The increasing interest in online shopping in recent years has increased the importance of understanding customer engagement valence (CEV) in a virtual service network. There is yet a comprehensive explanation of the CEV concept, particularly its impact on multi-actor networks such as web stores. Therefore, this study aims to fill this research gap. In this study, past literature in the marketing and consumer psychology field was critically reviewed to understand the concept of CEV in online shopping, and the propositional-based style was employed to conceptualize the CEV within the online shopping (web stores) context. The outcomes demonstrate that the valence of customer engagement is dependent on the cognitive interpretation of signals that are prompted by multiple actors on a web store service network. If the signals are positively interpreted, positive outcomes such as service co-creation are expected, but if they are negatively interpreted, negative outcomes such as service co-destruction are predicted. These notions create avenues for future empirical research and practical implications.
Fulltext Review of EEG, ERP, and Brain Connectivity Estimators as Predictive Biomarkers of Social Anxiety Disorder

Al-Ezzi A, Kamel N, Faye I, Gunaseli E

Front Psychol, 2020;11:730.
PMID: 32508695 DOI: 10.3389/fpsyg.2020.00730

Social anxiety disorder (SAD) is characterized by a fear of negative evaluation, negative self-belief and extreme avoidance of social situations. These recurrent symptoms are thought to maintain the severity and substantial impairment in social and cognitive thoughts. SAD is associated with a disruption in neuronal networks implicated in emotional regulation, perceptual stimulus functions, and emotion processing, suggesting a network system to delineate the electrocortical endophenotypes of SAD. This paper seeks to provide a comprehensive review of the most frequently studied electroencephalographic (EEG) spectral coupling, event-related potential (ERP), visual-event potential (VEP), and other connectivity estimators in social anxiety during rest, anticipation, stimulus processing, and recovery states. A search on Web of Science provided 97 studies that document electrocortical biomarkers and relevant constructs pertaining to individuals with SAD. This study aims to identify SAD neuronal biomarkers and provide insight into the differences in these biomarkers based on EEG, ERPs, VEP, and brain connectivity networks in SAD patients and healthy controls (HC). Furthermore, we proposed recommendations to improve methods of delineating the electrocortical endophenotypes of SAD, e.g., a fusion of EEG with other modalities such as functional magnetic resonance imaging (fMRI) and magnetoencephalograms (MEG), to realize better effectiveness than EEG alone, in order to ultimately evolve the treatment selection process, and to review the possibility of using electrocortical measures in the early diagnosis and endophenotype examination of SAD.
Fulltext Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Jawed S, Amin HU, Malik AS, Faye I

Front Behav Neurosci, 2019;13:86.
PMID: 31133829 DOI: 10.3389/fnbeh.2019.00086

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8-10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbor (k-NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k-NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k-NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.
Fulltext Neural correlates of eye contact in face-to-face verbal interaction: An EEG-based study of the extraversion personality trait

Roslan NS, Izhar LI, Faye I, Amin HU, Mohamad Saad MN, Sivapalan S, et al.

PLoS One, 2019;14(7):e0219839.
PMID: 31344061 DOI: 10.1371/journal.pone.0219839

The extraversion personality trait has a positive correlation with social interaction. In neuroimaging studies, investigations on extraversion in face-to-face verbal interactions are still scarce. This study presents an electroencephalography (EEG)-based investigation of the extraversion personality trait in relation to eye contact during face-to-face interactions, as this is a vital signal in social interactions. A sample of healthy male participants were selected (consisting of sixteen more extraverted and sixteen less extraverted individuals) and evaluated with the Eysenck's Personality Inventory (EPI) and Big Five Inventory (BFI) tools. EEG alpha oscillations in the occipital region were measured to investigate extraversion personality trait correlates of eye contact during a face-to-face interaction task and an eyes-open condition. The results revealed that the extraversion personality trait has a significant positive correlation with EEG alpha coherence in the occipital region, presumably due to its relationship with eye contact during the interaction task. Furthermore, the decrease in EEG alpha power during the interaction task compared to the eyes-open condition was found to be greater in the less extraverted participants; however, no significant difference was observed between the less and more extraverted participants. Overall, these findings encourage further research towards the understanding of neural mechanism correlates of the extraversion personality trait-particularly in social interaction.
Pulse waveform analysis on temporal changes in ocular blood flow due to caffeine intake: a comparative study between habitual and non-habitual groups

Ismail A, Bhatti MS, Faye I, Lu CK, Laude A, Tang TB

Graefes Arch Clin Exp Ophthalmol, 2018 Sep;256(9):1711-1721.
PMID: 29876732 DOI: 10.1007/s00417-018-4030-9

PURPOSE: To evaluate and compare the temporal changes in pulse waveform parameters of ocular blood flow (OBF) between non-habitual and habitual groups due to caffeine intake.
METHOD: This study was conducted on 19 healthy subjects (non-habitual 8; habitual 11), non-smoking and between 21 and 30 years of age. Using laser speckle flowgraphy (LSFG), three areas of optical nerve head were analyzed which are vessel, tissue, and overall, each with ten pulse waveform parameters, namely mean blur rate (MBR), fluctuation, skew, blowout score (BOS), blowout time (BOT), rising rate, falling rate, flow acceleration index (FAI), acceleration time index (ATI), and resistive index (RI). Two-way mixed ANOVA was used to determine the difference between every two groups where p
Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA

Javed E, Faye I, Malik AS, Abdullah JM

J Neurosci Methods, 2017 11 01;291:150-165.
PMID: 28842191 DOI: 10.1016/j.jneumeth.2017.08.020

BACKGROUND: Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.
METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.
RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.
COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.
CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.
Blood vessel segmentation in color fundus images based on regional and Hessian features

Shah SAA, Tang TB, Faye I, Laude A

Graefes Arch Clin Exp Ophthalmol, 2017 Aug;255(8):1525-1533.
PMID: 28474130 DOI: 10.1007/s00417-017-3677-y

PURPOSE: To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis.
METHODS: Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel.
RESULTS: The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy.
CONCLUSIONS: Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.
Fulltext Automated microaneurysm detection in diabetic retinopathy using curvelet transform

Ali Shah SA, Laude A, Faye I, Tang TB

J Biomed Opt, 2016 Oct;21(10):101404.
PMID: 26868326 DOI: 10.1117/1.JBO.21.10.101404

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.
Fulltext Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter

Babiker A, Faye I, Prehn K, Malik A

Front Psychol, 2015;6:1921.
PMID: 26733912 DOI: 10.3389/fpsyg.2015.01921

Pupil diameter (PD) has been suggested as a reliable parameter for identifying an individual's emotional state. In this paper, we introduce a learning machine technique to detect and differentiate between positive and negative emotions. We presented 30 participants with positive and negative sound stimuli and recorded pupillary responses. The results showed a significant increase in pupil dilation during the processing of negative and positive sound stimuli with greater increase for negative stimuli. We also found a more sustained dilation for negative compared to positive stimuli at the end of the trial, which was utilized to differentiate between positive and negative emotions using a machine learning approach which gave an accuracy of 96.5% with sensitivity of 97.93% and specificity of 98%. The obtained results were validated using another dataset designed for a different study and which was recorded while 30 participants processed word pairs with positive and negative emotions.

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