In recent years, Electroencephalographic analysis has gained prominence in stress research when combined with AI and Machine Learning models for validation. In this study, a lightweight dynamic brain connectivity framework based on Time Varying Directed Transfer Function is proposed, where TV DTF features were validated through ML based stress classification. TV DTF estimates the directional information flow between brain regions across distinct EEG frequency bands, thereby capturing temporal and causal influences that are often overlooked by static functional connectivity measures. EEG recordings from the 32 channel SAM 40 dataset were employed, focusing on mental arithmetic task trials. The dynamic EEG-based TV-DTF features were validated through ML classifiers such as Support Vector Machine, Random Forest, Gradient Boosting, Adaptive Boosting, and Extreme Gradient Boosting. Experimental results show that alpha-TV-DTF provided the strongest discriminative power, with SVM achieving 89.73% accuracy in 3-class classification and with XGBoost achieving 93.69% accuracy in 2 class classification. Relative to absolute power and phase locking based functional connectivity features, alpha TV DTF and beta TV DTF achieved higher performance across the ML models, highlighting the advantages of dynamic over static measures. Feature importance analysis further highlighted dominant long-range frontal parietal and frontal occipital informational influences, emphasizing the regulatory role of frontal regions under stress. These findings validate the lightweight TV-DTF as a robust framework, revealing spatiotemporal brain dynamics and directional influences across different stress levels.

If you work in an office, whether at home or on-site, there's a good chance you spend most of your day staring at a computer monitor. And you may spend even more time in front of a monitor at home playing PC games, browsing the web, or working on a personal project. That makes the quality of a monitor important. But how, exactly, should it be judged? PCWorld's experts have poured years of expertise into tackling this problem.

Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse structural and functional features in exploring the brain network. In this paper, a novel brain structure-function fusing-representation learning (BSFL) model is proposed to effectively learn fused representation from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the decomposition-fusion framework is developed to first decompose the feature space into the union of the uniform and the unique spaces for each modality, and then adaptively fuse the decomposed features to learn MCI-related representation. Moreover, a knowledge-aware transformer module is designed to automatically capture local and global connectivity features throughout the brain. Also, a uniform-unique contrastive loss is further devised to make the decomposition more effective and enhance the complementarity of structural and functional features. The extensive experiments demonstrate that the proposed model achieves better performance than other competitive methods in predicting and analyzing MCI. More importantly, the proposed model could be a potential tool for reconstructing unified brain networks and predicting abnormal connections during the degenerative processes in MCI.

The Indian automotive industry is on the edge of disruption due to increasing automation, new business models and digitization. This disruption is also through innovation and transformational change as industry players are adapting to shifting preferences on car ownership and new technological developments such as Autonomous Vehicles (AVs), IoT, cloud and proliferation electric and connected vehicles. Apart from electric and connected vehicles, the auto industry is also adopting technologies like cloud and IoT to improve the driving experience. From design and operation to servicing, cloud technology will be increasingly used at every stage to reduce costs and eliminate any scope for wastage. Cloud computing enables better vehicle engineering and thanks to advanced analytic capabilities, design teams can deliver exactly what customers want.

As internet connectivity becomes ubiquitous, everything around us is getting upgraded. We are ditching our old regular devices and getting on-board with the latest smart devices. This is what is happening with our car purchases as well. We are leaning towards smarter cars. These are deployed with many features which increases our safety standards and now they made their way to India.

We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure. We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of various connectivity features consisting of time and frequency-domain metrics of effective connectivity based on vector autoregressive model and partial directed coherence, and complex network measures of network topology. We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of 1D and 2D CNNs to integrate the features from various domains and dimensions using different fusion strategies. Hierarchical latent representations learned by the multiple convolutional layers from EEG connectivity reveal apparent group differences between SZ and healthy controls (HC). Results on a large resting-state EEG dataset show that the proposed CNNs significantly outperform traditional support vector machine classifiers. The MDC-CNN with combined connectivity features further improves performance over single-domain CNNs using individual features, achieving remarkable accuracy of $93.06\%$ with a decision-level fusion. The proposed MDC-CNN by integrating information from diverse brain connectivity descriptors is able to accurately discriminate SZ from HC. The new framework is potentially useful for developing diagnostic tools for SZ and other disorders.

The shift of focus from'under the hood' to the'car console' has opened doors for advanced technologies such as artificial intelligence, cloud computing, big data and machine learning into the automobile world. These technologies are instrumental in bridging the gap between fiction and reality when we talk about connected and autonomous vehicles. Here's a how Microsoft (MSFT) has made inroads into the connected car space. Cognizant defines the connected car as, "a vehicle using mechatronics, telematics and artificial intelligence technologies to interact with the environment to provide greater safety, comfort, entertainment and, importantly, a'connected-life' experience." The connected car is deemed to save considerable time and resources, while making mobility more efficient, safer and enjoyable.

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification. While non-invasive imaging measurements, such as the rest state fMRI, are typically used in this problem, it can be beneficial to incorporate a wide variety of non-imaging features, including personal and socio-cultural traits, into predictive modeling. We propose to employ a graph-based approach for combining both types of feature, where a contextual graph encodes the traits of a larger population while the brain activity patterns are defined as a multivariate function at the nodes of the graph. Since the underlying graph dictates the performance of the resulting predictive models, we explore the use of different graph construction strategies. Furthermore, we develop a bootstrapped version of graph convolutional neural networks (G-CNNs) that utilizes an ensemble of weakly trained G-CNNs to avoid overfitting and also reduce the sensitivity of the models on the choice of graph construction. We demonstrate its effectiveness on the Autism Brain Imaging Data Exchange (ABIDE) dataset and show that the proposed approach outperforms state-of-the-art approaches for this problem.