Recent Trends in Non-invasive Neural Recording Based Brain-to-Brain Synchrony Analysis on Multidisciplinary Human Interactions for Understanding Brain Dynamics: A Systematic Review.

The study of brain-to-brain synchrony has a burgeoning application in the brain-computer interface (BCI) research, offering valuable insights into the neural underpinnings of interacting human brains using numerous neural recording technologies. The area allows exploring the commonality of brain dynamics by evaluating the neural synchronization among a group of people performing a specified task. The growing number of publications on brain-to-brain synchrony inspired the authors to conduct a systematic review using the PRISMA protocol so that future researchers can get a comprehensive understanding of the paradigms, methodologies, translational algorithms, and challenges in the area of brain-to-brain synchrony research. This review has gone through a systematic search with a specified search string and selected some articles based on pre-specified eligibility criteria. The findings from the review revealed that most of the articles have followed the social psychology paradigm, while 36% of the selected studies have an application in cognitive neuroscience. The most applied approach to determine neural connectivity is a coherence measure utilizing phase-locking value (PLV) in the EEG studies, followed by wavelet transform coherence (WTC) in all of the fNIRS studies. While most of the experiments have control experiments as a part of their setup, a small number implemented algorithmic control, and only one study had interventional or a stimulus-induced control experiment to limit spurious synchronization. Hence, to the best of the authors' knowledge, this systematic review solely contributes to critically evaluating the scopes and technological advances of brain-to-brain synchrony to allow this discipline to produce more effective research outcomes in the remote future.

Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis.

BACKGROUND: With the growing number of the aged population, the number of Parkinson's disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients' symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries. METHOD: This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients' feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress. RESULT: The highest accuracy in PD detection using offline data was [Formula: see text] from voice data and [Formula: see text] from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient's gender, we could improve the detection accuracy. This study's novelty is the application of ensemble averaging on the combined decisions generated from the analysis of voice and tremor data. The average accuracy of PD detection becomes [Formula: see text] when ensemble averaging was performed on majority-vote from kNN, SVM, and NB. CONCLUSION: The proposed system can detect PD using a cloud-based system for computation, data preserving, and regular monitoring of voice and tremor samples captured by smartphones. Thus, this system can be a solution for healthcare authorities to ensure the older population's accessibility to a better medical diagnosis system in the developing countries, especially in the pandemic situation like COVID-19, when in-person monitoring is minimal.

HASAN: Highly accurate sensitivity for auto-contrast-corrected pMRI reconstruction.

A novel method for highly accurate coil sensitivity-map estimation, based on a constrained image-domain multi-channel LMS (c-iMCLMS) algorithm, is proposed for image reconstruction using self-calibrating SENSE. The sensitivity information is extracted by developing an image-domain cross-relation equation using the low-resolution images constructed from the fully sampled central region of the variable density MR data. Then this formulation is solved in an iterative way using a novel sum-of-squares (SOS) constraint. The improvement of the convergence speed of the c-iMCLMS algorithm is accomplished by SOS normalization of the low resolution image data and using a variable step-size in the update equation. The salient feature of the proposed technique is that it does not require any prior selection of the basis function and/or simultaneous estimation of the object image and the coil sensitivity-map. Only the low resolution images are re-filtered for the compensation of the data truncation effect to improve the consistency of the estimated coil maps. Besides, the application of the novel SOS-constraint, estimated using the pixel position-wise variance of the coil maps, gives closest to the true sensitivity-map. As a result, true object image with auto-corrected contrast is reconstructed without adopting any traditional post-contrast correction techniques. For minimization of the process noise, regularized conjugate gradient (CG) based SENSE reconstruction algorithm is used for image reconstruction using the estimated coil sensitivity-map. The proposed technique is tested on various simulation, synthetic and in-vivo datasets and significant signal-to-artifact-noise-ratio (SANR) improvement closest to the theoretical limit set by coil geometric factor is obtained as compared to some noted techniques in the literature both visually and numerically.