Publications by authors named "Mehrin Kiani"

In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI).

View Article and Find Full Text PDF

In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers.

View Article and Find Full Text PDF

With an increase in consumer demand of video gaming entertainment, the game industry is exploring novel ways of game interaction such as providing direct interfaces between the game and the gamers' cognitive or affective responses. In this work, gamer's brain activity has been imaged using functional near infrared spectroscopy (fNIRS) whilst they watch video of a video game (League of Legends) they play. A video of the face of the participants is also recorded for each of a total of 15 trials where a trial is defined as watching a gameplay video.

View Article and Find Full Text PDF
Article Synopsis
  • * This research introduces the use of electrocardiography (ECG) and electromyography (EMG) to identify breathing and movement-related sleep disorders, employing advanced signal processing techniques.
  • * A deep learning framework was developed to classify subjects into four categories, achieving a mean accuracy of 72% and a weighted F1 score of 0.57 in distinguishing between healthy individuals and those with specific sleep disorders like obstructive sleep apnea (OSA) and restless leg syndrome (RLS).
View Article and Find Full Text PDF