Our new online ARX model based tracking method allows continuous assessment of the transient coupling between the electrophysiological (EEG) and the hemodynamic (NIRS) signals representing resting-state spontaneous brain activation during anodal tDCS.

This paper presents a point of care testing device for neurovascular coupling (NVC) from simultaneous recording of electroencephalogram (EEG) and near infrared spectroscopy (NIRS) during anodal transcranial direct current stimulation (tDCS). Here, anodal tDCS modulated cortical neural activity leading to hemodynamic response can be used to identify the impaired cerebral microvessels functionality. The impairments in the cerebral microvessels functionality may lead to impairments in the cerebrovascular reactivity (CVR), where severely reduced CVR predicts the chances of transient ischemic attack and ipsilateral stroke. The neural and hemodynamic responses to anodal tDCS were studied through joint imaging with EEG and NIRS, where NIRS provided optical measurement of changes in tissue oxy-(\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$HbO2)$ \end{document} and deoxy-(\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$Hb$ \end{document}) hemoglobin concentration and EEG captured alterations in the underlying neuronal current generators. Then, a cross-correlation method for the assessment of NVC underlying the site of anodal tDCS is presented. The feasibility studies on healthy subjects and stroke survivors showed detectable changes in the EEG and the NIRS responses to a 0.526 A/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathrm{m}^{2}$ \end{document} of anodal tDCS. The NIRS system was bench tested on 15 healthy subjects that showed a statistically significant (p < 0.01) difference in the signal-to-noise ratio (SNR) between the ON- and OFF-states of anodal tDCS where the mean SNR of the NIRS device was found to be 42.33 ± 1.33 dB in the ON-state and 40.67 ± 1.23 dB in the OFF-state. Moreover, the clinical study conducted on 14 stroke survivors revealed that the lesioned hemisphere with impaired circulation showed significantly (p < 0.01) less change in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$HbO2$ \end{document} than the nonlesioned side in response to anodal tDCS. The EEG study on healthy subjects showed a statistically significant (p < 0.05) decrease around individual alpha frequency in the alpha band (8−13 Hz) following anodal tDCS. Moreover, the joint EEG-NIRS imaging on 4 stroke survivors showed an immediate increase in the theta band (4−8 Hz) EEG activity after ...

Transcranial direct current stimulation (tDCS) has been shown to modulate corticospinal excitability. We used near-infrared spectroscopy (NIRS)-electroencephalography (EEG) joint-imaging during and after anodal tDCS to measure changes in mean cerebral haemoglobin oxygen saturation (rSO2) along with changes in the log-transformed mean-power of EEG within 0.5 Hz-11.25 Hz. In two separate studies, we investigated local post-tDCS alterations from baseline at the site of anodal tDCS using NIRS-EEG/tDCS joint-imaging as well as local post-tDCS alterations in motor evoked potentials (MEP)-measure of corticospinal excitability. In the first study, we found that post-tDCS changes in the mean rSO2 from baseline mostly correlated with the corresponding post-tDCS change in log-transformed mean-power of EEG within 0.5 Hz-11.25 Hz. Moreover, a decrease in log-transformed mean-power of EEG within 0.5 Hz-11.25 Hz corresponded with an increase in the MEP-measure of corticospinal excitability--found in the second study. Therefore, we propose to combine NIRS-EEG/tDCS joint-imaging with corticospinal excitability investigation in a single study to confirm these finding. Furthermore, we postulate that the innovative technologies for portable NIRS-EEG neuroimaging may be leveraged to objectively quantify the progress (e.g., corticospinal excitability alterations) and dose tDCS intervention as an adjuvant treatment during neurorehabilitation.

Transcranial direct current stimulation (tDCS) has been shown to modulate neural activity. Neural activity has been shown to be closely related, spatially and temporally, to cerebral blood flow (CBF) that supplies glucose via neurovascular coupling. Therefore, noninvasive and continuous monitoring of neural activity is possible with a measure of cerebral hemoglobin oxygenation using near-infrared spectroscopy (NIRS). In principal accordance, NIRS can capture the hemodynamic response to tDCS but the challenge remains in removing the systemic interference occurring in the superficial layers of the head that are also affected by tDCS. An approach may be to use short optode separations to measure systemic hemodynamic fluctuations occurring in the superficial layers which can then be used as regressors to remove the systemic contamination. Here, we demonstrate that temporal artery tap may be used to better identify systemic interference using this short-separation NIRS. Moreover, NIRS-EEG joint-imaging during anodal tDCS was used to measure changes in mean cerebral haemoglobin oxygen saturation (rSO2) along with changes in the log-transformed mean-power of EEG within 0.5 Hz-11.25 Hz. We found that percent change in the mean rSO2 better correlated with the corresponding percent change in log-transformed mean-power of EEG within 0.5 Hz-11.25 Hz frequency band after removing the systemic contamination using the temporal artery tap method. Based on our findings, we propose that anterior temporal artery tap technique presented in this paper may be able to classify carotid stenosis, external carotid artery stenosis, and internal carotid artery stenosis patients using the laterality in the hemodynamic response evoked by anodal tDCS both at the brain as well as at the superficial layers. These findings may have important implications for both prognosis and rehabilitation of patients with intracranial stenosis.

The primary goal of trending technology artificial intelligence (AI) is to realize natural human-machine dialogue. Various IT-based companies also utilized dialogue networks technology to create various types of Virtual Personal Assistants focused on their products and areas for expanding human-machine contact, such as Alexa, Cortana, Google's Assistant, Siri and so more. Just like the Microsoft voice assistant named 'Cortana', we designed our virtual assistant which performs basic tasks based on the instruction provided to it on the Windows platform using Python. Here, Python is used as a scripting language as it has a large library that is used to perform instructions. Using Python packages, a personalized virtual assistant recognizes and processes the user's voice. Voice assistants are a fantastic advancement in the sector of Artificial Intelligence that can transform people's lives in a variety of ways. The voice-based assistant was initially given on cellphones and quickly gained popularity. It was widely acknowledged by all. Previously, voice assistants were largely found in smartphones and laptops, but they are now increasingly available in various home automation setups and smart speakers. Many technologies seem to become wiser in their very own way, allowing them to converse with humans in a simple language. Desktop voice assistants are programme that can identify people's speech and answer through an integrated speech system. This paper will outline how different voice assistants work, as well as their primary challenges and limitations. The way of developing a voice-based assistant without requiring cloud services is discussed in this paper, which would promote the future growth of such devices. Keywords: Voice Assistant, Speech Recognition, Python, Smtplib, Automation.