Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG.

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles. Despite their potential, offline processing is often necessary to eliminate artifacts and extract features, making these methods unsuitable for real-time applications. The dataset was chosen with 34 healthy subjects to measure their EEG signals during resting states (eyes open and eyes closed) and while performing learning tasks. The subjects displayed no prior knowledge of the animated educational content presented in video format. The paper presents an analysis of EEG signals measured during a resting state with closed eyes using three deep learning techniques: Long-term, short-term memory (LSTM), Long-term, short-term memory-convolutional neural network (LSTM-CNN), and Long-term, short-term memory-Fully convolutional neural network (LSTM-FCNN). The chosen techniques were based on their suitability for real-time applications with varying data lengths and the need for less computational time. The optimization of hypertuning parameters has enabled the identification of visual learners through the implementation of three techniques. LSTM-CNN technique has the highest average accuracy of 94%, a sensitivity of 80%, a specificity of 92%, and an F1 score of 94% when identifying the visual learning style of the student out of all three techniques. This research has shown that the most effective method is the deep learning-based LSTM-CNN technique, which accurately identifies a student's visual learning style.

Impact of perceived social support and psychological capital on university students' academic success: testing the role of academic adjustment as a moderator.

BACKGROUND: Academic adjustment is a significant predictor of the academic success of students. The aim of this study is to examine how academic adjustment plays an important role as a moderator in perceived social support, psychological capital, and success outcome relationships among university students. METHODS: Three hundred seventy-three valid questionnaires were collected from different departments of different universities using convenience sampling method. Smart PLS 3.0 was used for data analysis. RESULTS: The study results indicated that perceived social support and psychological capital have a significant direct impact on academic adjustment and academic success. The results of the study also demonstrated that the relationships between perceived social support, psychological capital, and successful outcomes are partially and moderated by academic adjustment. CONCLUSION: This research develops a predictive model for examining students' academic adjustment to university and the outcomes of success based on social capital theory and conservation of resources theory. The current study suggests that it is necessary for policymakers to make full use of their ability to enable students to adjust to university life effectively. Higher education institutions should therefore pay full attention to the development of students' academic skills that contribute to academic success.

Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques.

BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers. CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm.

Our emotions and sentiments are influenced by naturalistic stimuli such as the movies we watch and the songs we listen to, accompanied by changes in our brain activation. Comprehension of these brain-activation dynamics can assist in identification of any associated neurological condition such as stress and depression, leading towards making informed decision about suitable stimuli. A large number of open-access functional magnetic resonance imaging (fMRI) datasets collected under naturalistic conditions can be used for classification/prediction studies. However, these datasets do not provide emotion/sentiment labels, which limits their use in supervised learning studies. Manual labeling by subjects can generate these labels, however, this method is subjective and biased. In this study, we are proposing another approach of generating automatic labels from the naturalistic stimulus itself. We are using sentiment analyzers (VADER, TextBlob, and Flair) from natural language processing to generate labels using movie subtitles. Subtitles generated labels are used as the class labels for positive, negative, and neutral sentiments for classification of brain fMRI images. Support vector machine, random forest, decision tree, and deep neural network classifiers are used. We are getting reasonably good classification accuracy (42-84%) for imbalanced data, which is increased (55-99%) for balanced data.

Impact of academic cheating and perceived online learning effectiveness on academic performance during the COVID-19 pandemic among Pakistani students.

Objective: The study was conducted to examine academic cheating behaviors and perceived online effectiveness on academic performance during the period of COVID-19 among schools, colleges, and university students in Pakistan. Methodology: A cross-sectional research design was used in the current study. Convenience sampling was used to collect the data. The study included a total sample of N = 8,590 students, with males (n = 3,270, 38%) and females (n = 5,320, 61%) participating. The data was divided into three categories: high schools (n = 1,098, 12.7%), colleges (n = 4,742, 55.2%), and universities (n = 2,570, 32.1%). School students had an average age of (M = 15, SD = 4.65), college students had an average age of (M = 20, SD = 5.64), and university students had an average age of (M = 24, SD = 5.01). Result: The results indicated that 60% of students admitted to cheating during online exams most of the time; 30% of students admitted to cheating at least once during an online exam. The study found that students (from high school, college, and university) obtained higher grades in online exams as compared to physical exams. Furthermore, significant gender differences were found on the scales of online learning effectiveness in school, college, and university students (t = 2.3*, p = 0.05 vs. t = 4.32**, p = 0.000 vs. t = -3.3*, p = 0.04). Similarly, on the scale of academic performance, students have significant gender differences. Multivariate regression analysis confirms that students' 26% academic performance was increased due to cheating (F (2, 8,588) = 16.24, p = 0.000). Students believe online learning is effective because academic grades are easily obtained. Conclusion: Cheating is more common and easier in online courses, according to more than half of respondents, and they take advantage of this. Academicians are heavily encouraged to develop morality and ethics in their students so that their institutions can produce ethical professionals for the educational community.

Diabetes Physical Examination.

The physical examination of the patient with diabetes may have revealed findings that confirm the diagnosis, classify the type of diabetes, and begin to evaluate for the macro- and microvascular complications of diabetes and significant comorbid conditions. While screening for the diagnosis of diabetes occurs with assessment for abnormal blood glucose, given the high rates of morbidity and mortality associated with diabetes, utilization of the physical examination plays a key role in identifying patients at risk for the complications of diabetes. The discussion of elements of the physical examination relevant to the patient with diabetes, both type 1 and type 2, will be discussed in this article.

Microscopic techniques for fabrication of polyethersulfone thin-film nanocomposite membranes intercalated with UiO-66-SO3 H for heavy metal ions removal from water.

Environmental remediation of heavy metals from wastewater is becoming popular area in the field of membrane technology. Heavy metals are toxic in nature and have ability to bioaccumulate in water bodies. In current study, zirconium-based metal organic frameworks (MOFs), that is, UiO-66 and UiO-66-SO3 H with a mean diameter of 200 nm were synthesized and intercalated into polyethersulfone (PES) substrate to fabricate thin-film nanocomposite (TFN) membranes via an interfacial polymerization (IP) method. TFN membranes exhibit higher selectivity and permeability as compared to thin-film composite (TFC) membranes for heavy metals, such as cadmium (Cd) and mercury (Hg). Zirconium-based MOFs are highly stable in water and due to smaller pore size enhanced hydrophilicity of TFN membranes. In addition, TFN membrane with functionalized MOF (UiO-66-SO3 H) performed best as compared to TFC and TFN with UiO-66 MOF. The effect of loading of different weight percentages (wt%) of both MOFs for TFN membranes was also investigated. The TFN membranes with loading (0.2 wt%) of UiO-66-SO3 H displayed highest permeability of 9.57 LMH/bar and notable rejections of 90% and 87.7% toward Cd and Hg, respectively. To our best understanding, it is the first study of intercalating functionalized UiO-66-SO3 H in TFC membranes by IP and their application on heavy metals especially Cd and Hg.

Hemispheric Asymmetries in Electroencephalogram Oscillations for Long-Term Memory Retrieval in Healthy Individuals.

The hemispherical encoding retrieval asymmetry (HERA) model, established in 1991, suggests that the involvement of the right prefrontal cortex (PFC) in the encoding process is less than that of the left PFC. The HERA model was previously validated for episodic memory in subjects with brain traumas or injuries. In this study, a revised HERA model is used to investigate long-term memory retrieval from newly learned video-based content for healthy individuals using electroencephalography. The model was tested for long-term memory retrieval in two retrieval sessions: (1) recent long-term memory (recorded 30 min after learning) and (2) remote long-term memory (recorded two months after learning). The results show that long-term memory retrieval in healthy individuals for the frontal region (theta and delta band) satisfies the revised HERA asymmetry model.

Metagenomic analysis of relative abundance and diversity of bacterial microbiota in Bemisia tabaci infesting cotton crop in Pakistan.

B. tabaci species complex are among the world's most devastating agricultural pests causing economic losses by direct feeding and more importantly by transmitting plant viruses like cotton leaf curl disease (CLCuD) associated viruses to cultivated cotton in Pakistan. Taxonomic diversity of B. tabaci associated bacterial communities using NGS techniques so far is reported from insects grown on artificial diet under lab conditions. In this study 16S rDNA metagenome sequencing analysis was used to characterize bacterial compositions in wild adult B. tabaci infesting cultivated cotton in eight major cotton growing districts of southern Punjab, Pakistan. We have identified 50 known and 7 unknown genera of bacteria belonging to 10 phyla, 20 classes, 30 orders and 40 families. Beta diversity analysis of our data sets reveal that whiteflies infesting cotton in geographically distinct locations had similar bacterial diversity. These results for the first time provide insights into the microbiome diversity of wild type whiteflies infesting a cultivated crop.

Radiotherapy-Associated Paraneoplastic Pemphigus and Its Complications in a Patient with Nasopharyngeal Carcinoma.

Paraneoplastic pemphigus arising in association with non-haematological cancers is extremely rare, and there are no reported cases of a patient developing this in the setting of nasopharyngeal carcinoma and only 2 reported cases of patients developing this in response to radiotherapy. Here, we present the case of a patient who developed radiotherapy-associated paraneoplastic pemphigus in the setting of nasopharyngeal carcinoma and who then developed multiple complications. LEARNING POINTS: Paraneoplastic pemphigus has a remarkably similar clinical picture to other dermatologic diseases (for example, pemphigus vulgaris), and therefore, it is easy to miss the diagnosis.Patients with oral and mucocutaneous lesions refractory to conventional therapy should be worked up for underlying occult malignancy.Our case highlights that prompt diagnosis and initiation of immunosuppressive therapy alongside effective management of complications can ensure recovery and survival.

Determination of Iron Chelating Agents by Analytical Methods: A Review.

Acute iron poisoning and chronic iron overload consequences in significant morbidity and mortality worldwide. Treatment of acute iron poisoning and chronic iron overload can be challenging and care providers are often tackled with management dilemmas. Iron chelating agents are commonly prescribed for patients with iron deficiency anemia. In this review article, different analytical techniques are reported used for qualitative and quantitative analysis of iron chelating agents like, deferiprone, deferoxamine, and deferasirox. Efforts are taken to collect all related articles published till October 2018. This review discusses all analytical methods, its advantages and disadvantages as well as its applications. This article will help you to know about basic analytical techniques as well as advanced hyphenated techniques practiced for determination of iron chelating agents in different matrices. The techniques discussed in this review follow the ICH guidelines for method validation.

Exploring EEG Effective Connectivity Network in Estimating Influence of Color on Emotion and Memory.

Color is a perceptual stimulus that has a significant impact on improving human emotion and memory. Studies have revealed that colored multimedia learning materials (MLMs) have a positive effect on learner's emotion and learning where it was assessed by subjective/objective measurements. This study aimed to quantitatively assess the influence of colored MLMs on emotion, cognitive processes during learning, and long-term memory (LTM) retention using electroencephalography (EEG). The dataset consisted of 45 healthy participants, and MLMs were designed in colored or achromatic illustrations to elicit emotion and that to assess its impact on LTM retention after 30-min and 1-month delay. The EEG signal analysis was first started to estimate the effective connectivity network (ECN) using the phase slope index and expand it to characterize the ECN pattern using graph theoretical analysis. EEG results showed that colored MLMs had influences on theta and alpha networks, including (1) an increased frontal-parietal connectivity (top-down processing), (2) a larger number of brain hubs, (3) a lower clustering coefficient, and (4) a higher local efficiency, indicating that color influences information processing in the brain, as reflected by ECN, together with a significant improvement in learner's emotion and memory performance. This is evidenced by a more positive emotional valence and higher recall accuracy for groups who learned with colored MLMs than that of achromatic MLMs. In conclusion, this paper demonstrated how the EEG ECN parameters could help quantify the influences of colored MLMs on emotion and cognitive processes during learning.

Bleeding detection in wireless capsule endoscopy videos - Color versus texture features.

Wireless capsule endoscopy (WCE) is an effective technology that can be used to make a gastrointestinal (GI) tract diagnosis of various lesions and abnormalities. Due to a long time required to pass through the GI tract, the resulting WCE data stream contains a large number of frames which leads to a tedious job for clinical experts to perform a visual check of each and every frame of a complete patient's video footage. In this paper, an automated technique for bleeding detection based on color and texture features is proposed. The approach combines the color information which is an essential feature for initial detection of frame with bleeding. Additionally, it uses the texture which plays an important role to extract more information from the lesion captured in the frames and allows the system to distinguish finely between borderline cases. The detection algorithm utilizes machine-learning-based classification methods, and it can efficiently distinguish between bleeding and nonbleeding frames and perform pixel-level segmentation of bleeding areas in WCE frames. The performed experimental studies demonstrate the performance of the proposed bleeding detection method in terms of detection accuracy, where we are at least as good as the state-of-the-art approaches. In this research, we have conducted a broad comparison of a number of different state-of-the-art features and classification methods that allows building an efficient and flexible WCE video processing system.

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities.

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.

SVD-based Tensor-Completion Technique for Background Initialization.

Extracting the background from a video in the presence of various moving patterns is the focus of several background-initialization approaches. To model the scene background using rank-one matrices, this paper proposes a background-initialization technique that relies on the singular-value decomposition (SVD) of spatiotemporally extracted slices from the video tensor. The proposed method is referred to as spatiotemporal slice-based SVD (SS-SVD). To determine the SVD components that best model the background, a depth analysis of the computation of the left/right singular vectors and singular values is performed, and the relationship with tensor-tube fibers is determined. The analysis proves that a rank-1 matrix extracted from the first left and right singular vectors and singular value represents an efficient model of the scene background. The performance of the proposed SS-SVD method is evaluated using 93 complex video sequences of different challenges, and the method is compared with state-of-the-art tensor/matrix completion-based methods, statistical-based methods, search-based methods, and labeling-based methods. The results not only show better performance over most of the tested challenges, but also demonstrate the capability of the proposed technique to solve the background-initialization problem in a less computational time and with fewer frames.

A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA.

Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.

FEM Analysis of Sezawa Mode SAW Sensor for VOC Based on CMOS Compatible AlN/SiO2/Si Multilayer Structure.

A Finite Element Method (FEM) simulation study is conducted, aiming to scrutinize the sensitivity of Sezawa wave mode in a multilayer AlN/SiO2/Si Surface Acoustic Wave (SAW) sensor to low concentrations of Volatile Organic Compounds (VOCs), that is, trichloromethane, trichloroethylene, carbon tetrachloride and tetrachloroethene. A Complimentary Metal-Oxide Semiconductor (CMOS) compatible AlN/SiO2/Si based multilayer SAW resonator structure is taken into account for this purpose. In this study, first, the influence of AlN and SiO2 layers’ thicknesses over phase velocities and electromechanical coupling coefficients (k²) of two SAW modes (i.e., Rayleigh and Sezawa) is analyzed and the optimal thicknesses of AlN and SiO2 layers are opted for best propagation characteristics. Next, the study is further extended to analyze the mass loading effect on resonance frequencies of SAW modes by coating a thin Polyisobutylene (PIB) polymer film over the AlN surface. Finally, the sensitivity of the two SAW modes is examined for VOCs. This study concluded that the sensitivity of Sezawa wave mode for 1 ppm of selected volatile organic gases is twice that of the Rayleigh wave mode.

A Comparative Study of Different EEG Reference Choices for Diagnosing Unipolar Depression.

The choice of an electroencephalogram (EEG) reference has fundamental importance and could be critical during clinical decision-making because an impure EEG reference could falsify the clinical measurements and subsequent inferences. In this research, the suitability of three EEG references was compared while classifying depressed and healthy brains using a machine-learning (ML)-based validation method. In this research, the EEG data of 30 unipolar depressed subjects and 30 age-matched healthy controls were recorded. The EEG data were analyzed in three different EEG references, the link-ear reference (LE), average reference (AR), and reference electrode standardization technique (REST). The EEG-based functional connectivity (FC) was computed. Also, the graph-based measures, such as the distances between nodes, minimum spanning tree, and maximum flow between the nodes for each channel pair, were calculated. An ML scheme provided a mechanism to compare the performances of the extracted features that involved a general framework such as the feature extraction (graph-based theoretic measures), feature selection, classification, and validation. For comparison purposes, the performance metrics such as the classification accuracies, sensitivities, specificities, and F scores were computed. When comparing the three references, the diagnostic accuracy showed better performances during the REST, while the LE and AR showed less discrimination between the two groups. Based on the results, it can be concluded that the choice of appropriate reference is critical during the clinical scenario. The REST reference is recommended for future applications of EEG-based diagnosis of mental illnesses.

A review on EEG-based methods for screening and diagnosing alcohol use disorder.

The screening test for alcohol use disorder (AUD) patients has been of subjective nature and could be misleading in particular cases such as a misreporting the actual quantity of alcohol intake. Although the neuroimaging modality such as electroencephalography (EEG) has shown promising research results in achieving objectivity during the screening and diagnosis of AUD patients. However, the translation of these findings for clinical applications has been largely understudied and hence less clear. This study advocates the use of EEG as a diagnostic and screening tool for AUD patients that may help the clinicians during clinical decision making. In this context, a comprehensive review on EEG-based methods is provided including related electrophysiological techniques reported in the literature. More specifically, the EEG abnormalities associated with the conditions of AUD patients are summarized. The aim is to explore the potentials of objective techniques involving quantities/features derived from resting EEG, event-related potentials or event-related oscillations data.