Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning.

We develop a multiexposure image fusion method based on texture features, which exploits the edge preserving and intraregion smoothing property of nonlinear diffusion filters based on partial differential equations (PDE). With the captured multiexposure image series, we first decompose images into base layers and detail layers to extract sharp details and fine details, respectively. The magnitude of the gradient of the image intensity is utilized to encourage smoothness at homogeneous regions in preference to inhomogeneous regions. Then, we have considered texture features of the base layer to generate a mask (i.e., decision mask) that guides the fusion of base layers in multiresolution fashion. Finally, well-exposed fused image is obtained that combines fused base layer and the detail layers at each scale across all the input exposures. Proposed algorithm skipping complex High Dynamic Range Image (HDRI) generation and tone mapping steps to produce detail preserving image for display on standard dynamic range display devices. Moreover, our technique is effective for blending flash/no-flash image pair and multifocus images, that is, images focused on different targets.

In this paper we propose a novel detail-enhancing exposure fusion approach using nonlinear translation-variant filter (NTF). With the captured Standard Dynamic Range (SDR) images under different exposure settings, first the fine details are extracted based on guided filter. Next, the base layers (i.e., images obtained from NTF) across all input images are fused using multiresolution pyramid. Exposure, contrast, and saturation measures are considered to generate a mask that guides the fusion process of the base layers. Finally, the fused base layer is combined with the extracted fine details to obtain detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions without High Dynamic Range Image (HDRI) representation and tone mapping step. Moreover, we have demonstrated that the proposed method is also suitable for the multifocus image fusion without introducing artifacts.

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the first to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future. A de novo biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify Chest X-Ray (CXR) scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease, thus classifying it on all subsequent occurrences. Second, the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks.

Nephropathia Epidemica (NE), endemic to several Volga regions of Russia, including the Republic of Tatarstan (RT) and the Republic of Mordovia (RM), is a mild form of hemorrhagic fever with renal syndrome caused by infection with rodent-borne orthohantaviruses. Although NE cases have been reported for decades, little is known about the hantavirus strains associated with human infection in these regions. There is also limited understanding of the pathogenesis of NE in the RT and the RM. To address these knowledge gaps, we conducted comparative analyses of patients with NE in the RT and the RM. Clinical symptoms were more severe in patients with NE from the RM with longer observed duration of fever symptoms and hospitalization. Analysis of patient sera showed changes in the levels of numerous cytokines, chemokines, and matrix metalloproteases (MMPs) in patients with NE from both the RT and the RM, suggesting leukocyte activation, extracellular matrix degradation, and leukocyte chemotaxis. Interestingly, levels of several cytokines were distinctly different between patients NE from the RT when compared with those from the RM. These differences were not related to the genetic variation of orthohantaviruses circulating in those regions, as sequence analysis showed that Puumala virus (PUUV) was the causative agent of NE in these regions. Additionally, only the “Russia” (RUS) genetic lineage of PUUV was detected in the serum samples of patients with NE from both the RT and the RM. We therefore conclude that differences in serum cytokine, chemokine, and MMP levels between the RT and the RM are related to environmental factors and lifestyle differences that influence individual immune responses to orthohantavirus infection.

Every individual's perception of multimedia content varies based on their interpretation. Therefore, it is quite challenging to predict likability of any multimedia just based on its content. This paper presents a novel system for analysis of facial expressions of subject against the multimedia content to be evaluated. First, we developed a dataset by recording facial expressions of subjects under uncontrolled environment. These subjects are volunteers recruited to watch the videos of different genre, and provide their feedback in terms of likability. Subject responses are divided into three categories: Like, Neutral and Dislike. A novel multimodal system is developed using the developed dataset. The model learns feature representation from data based on the three provided categories. The proposed system contains ensemble of time distributed convolutional neural network, 3D convolutional neural network, and long short term memory networks. All the modalities in proposed architecture are evaluated independently as well as in distinct combinations. The paper also provides detailed insight into learning behavior of the proposed system.