Analysing the brain networks corresponding to the facial contrast-chimeras.

How humans recognise faces and objects effortlessly, has become a great point of interest. To understand the underlying process, one of the approaches is to study the facial features, in particular ordinal contrast relations around the eye region, which plays a crucial role in face recognition and perception. Recently the graph-theoretic approaches to electroencephalogram (EEG) analysis are found to be effective in understating the underlying process of human brain while performing various tasks. We have explored this approach in face recognition and perception to know the importance of contrast features around the eye region. We studied functional brain networks, formed using EEG responses, corresponding to four types of visual stimuli with varying contrast relationships: Positive faces, chimeric faces (photo-negated faces, preserving the polarity of contrast relationships around eyes), photo-negated faces and only eyes. We observed the variations in brain networks of each type of stimuli by finding the distribution of graph distances across brain networks of all subjects. Moreover, our statistical analysis shows that positive and chimeric faces are equally easy to recognise in contrast to difficult recognition of negative faces and only eyes.

Environment Enrichment Facilitates Long-Term Memory Consolidation through Behavioral Tagging.

The behavioral tagging (BT) hypothesis provides crucial insights into the mechanism of long-term memory (LTM) consolidation. Novelty exposure in BT is a decisive step in activating the molecular machinery of memory formation. Several studies have validated BT using different neurobehavioral tasks; however, the novelty given in all studies is open field (OF) exploration. Environment enrichment (EE) is another key experimental paradigm to explore the fundamentals of brain functioning. Recently, several studies have highlighted the importance of EE in enhancing cognition, LTM, and synaptic plasticity. Hence, in the present study, we investigated the effects of different types of novelty on LTM consolidation and plasticity-related protein (PRP) synthesis using the BT phenomenon. Novel object recognition (NOR) was used as the learning task for rodents (male Wistar rats), while OF and EE were two types of novel experiences provided to the rodents. Our results indicated that EE exposure efficiently leads to LTM consolidation through the BT phenomenon. In addition, EE exposure significantly enhances protein kinase Mζ (PKMζ) synthesis in the hippocampus region of the rat brain. However, the OF exposure did not lead to significant PKMζ expression. Further, our results did not find alterations in BDNF expression after EE and OF exposure in the hippocampus. Hence, it is concluded that different types of novelty mediate the BT phenomenon up to the same extent at the behavioral level. However, the implications of different novelties may differ at molecular levels.

COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.

Automated detection of COVID-19 from CT scan using convolutional neural network.

Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model's diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.

Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG.

Time-varying neurophysiological activity has been classically explored using correlation based sliding window analysis. However, this method employs only lower order statistics to track dynamic functional connectivity of the brain. We introduce recursive dynamic functional connectivity (rdFC) that incorporates higher order statistics to generate a multi-order connectivity pattern by analyzing neurophysiological data at multiple time scales. The technique builds a hierarchical graph between various temporal scales as opposed to traditional approaches that analyze each scale independently. We examined more than a million rdFC patterns obtained from morphologically diverse EEGs of 2378 subjects of varied age and neurological health. Spatiotemporal evaluation of these patterns revealed three dominant connectivity patterns that represent a universal underlying correlation structure seen across subjects and scalp locations. The three patterns are both mathematically equivalent and observed with equal prevalence in the data. The patterns were observed across a range of distances on the scalp indicating that they represent a spatially scale-invariant correlation structure. Moreover, the number of patterns representing the correlation structure has been shown to be linked with the number of nodes used to generate them. We also show evidence that temporal changes in the rdFC patterns are linked with seizure dynamics.

Role of Yoga and Meditation as Complimentary Therapeutic Regime for Stress-Related Neuropsychiatric Disorders: Utilization of Brain Waves Activity as Novel Tool.

During recent decades, stress-related neuropsychiatric disorders such as anxiety, depression, chronic tension headache, and migraine have established their stronghold in the lives of a vast number of people worldwide. In order to address this global phenomenon, intensive studies have been carried out leading to the advancement of drugs like anti-depressants, anxiolytics, and analgesics which although help in combating the symptoms of such disorders but also create long-term side effects. Thus, as an alternative to such clinical practices, various complementary therapies such as yoga and meditation have been proved to be effective in alleviating the causes and symptoms of different neuropsychiatric disorders. The role of altered brain waves in this context has been recognized and needs to be pursued at the highest level. Thus, the current study provides a review focused on describing the effects of yoga and meditation on anxiety and depression as well as exploring brain waves as a tool for assessing the potential of these complementary therapies for such disorders.

Tracking the Transitions of Brain States: An Analytical Approach Using EEG.

OBJECTIVE: Classification of the neural activity of the brain is a well known problem in the field of brain computer interface. Machine learning based approaches for classification of brain activities do not reveal the underlying dynamics of the human brain. METHODS: Since eigen decomposition has been found useful in a variety of applications, we conjecture that change of brain states would manifest in terms of changes in the invariant spaces spanned by eigen vectors as well as amount of variance along them. Based on this, our first approach is to track the brain state transitions by analysing invariant space variations over time. Whereas, our second approach analyses sub-band characteristic response vector formed using eigen values along with the eigen vectors to capture the dynamics. RESULT: We have taken two real time EEG datasets to demonstrate the efficacy of proposed approaches. It has been observed that in case of unimodal experiment, invariant spaces explicitly show the transitions of brain states. Whereas sub-band characteristic response vector approach gives better performance in the case of cross-modal conditions. CONCLUSIONS: Evolution of invariant spaces along with the eigen values may help in understanding and tracking the brain state transitions. SIGNIFICANCE: The proposed approaches can track the activity transitions in real time. They do not require any training dataset.

Modeling and prediction of COVID-19 pandemic using Gaussian mixture model.

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.

Automated Epilepsy Diagnosis Using EEG With Test Set Evaluation.

Several electroencephalogram (EEG)-based predictive models for automated epilepsy diagnosis have been proposed over more than a decade. However, to the best of our knowledge, none have been evaluated on a holdout/test set. A vast majority of these studies have reported accuracies above 95% on a benchmark EEG dataset, but the dataset has been shown here to have certain limitations when used for building classifiers for epilepsy diagnosis. We implemented two previously reported classifiers trained on the benchmark dataset whose accuracies were observed to drop sharply when evaluated on a test set. We propose a feature, engineered specifically, for epilepsy diagnosis that attempts to characterize the neuronal synchronization using scalp EEG by extending the concept of the impulse response of linear time-invariant systems to matrices. This feature was tested on the EEG of 50 epileptics and 50 healthy subjects and yielded an area under the curve (AUC) of 0.87. It outperforms the existing models implemented by us that gave the AUC of 0.80 when trained and tested on scalp EEG data, thereby, setting the new benchmark for automated epilepsy diagnosis on test set evaluation. The feature has also been shown to have statistical consistency across time and vigilance states with robustness against EEG artifacts.

The Fourier decomposition method for nonlinear and non-stationary time series analysis.

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time-frequency-energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.

Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition.

A new method for removing the baseline wander (BW) noise based on multivariate empirical mode decomposition is presented. The proposed method is compared with recently introduced technique for BW removal using Hilbert vibration decomposition in terms of correlation coefficient criterion and signal-to-noise ratio. To evaluate the performance of the proposed method, real BW signals are added to synthetic and clinical electrocardiogram (ECG) signals. It is shown that presented methodology has significant scope of removing BW noise in real world ECG signals.

Text extraction and document image segmentation using matched wavelets and MRF model.

In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.

An iterative approach to design a SAW filter having desired response.

Present work proposes an iterative technique to estimate reflection weighting function and transduction weighting function simultaneously to achieve a desired single-phase unidirectional transducer (SPUDT) response. The technique uses the p-matrix formulation in order to describe characteristics of a SPUDT. Simulation results are presented to show the validity of the approach.