Explainable artificial intelligence model to predict brain states from fNIRS signals.

Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI) system that can decompose the Deep Learning mode's output onto the input variables for fNIRS signals is described here. We propose an xAI-fNIRS system that consists of a classification module and an explanation module.

Functional Connectivity of Language Regions of Stroke Patients with Expressive Aphasia During Real-Time Functional Magnetic Resonance Imaging Based Neurofeedback.

Stroke lesions in the language centers of the brain impair the language areas and their connectivity. This article describes the dynamics of functional connectivity (FC) of language areas (FCL) during real-time functional magnetic resonance imaging (RT-fMRI)-based neurofeedback training for poststroke patients with expressive aphasia. The hypothesis is that FCL increases during the upregulation of language areas during neurofeedback training and that the training improves FCL with an increasing number of sessions and restores it toward normalcy.

Self-regulation of language areas using real-time functional MRI in stroke patients with expressive aphasia.

The objectives of this study were to test (i) If stroke patients with expressive Aphasia could learn to up-regulate the Blood Oxygenation Level Dependent (BOLD) signal in language areas of the brain, namely Inferior Frontal Gyrus (Broca's area) and Superior Temporal Gyrus (Wernicke's area), with real-time fMRI based neurofeedback of the BOLD activation and functional connectivity between the language areas; and (ii) acquired up-regulation could lead to an improvement in expression of language. The study was performed on three groups: Group 1 (n = 4) of Test patients and group 2 (n = 4) of healthy volunteers underwent the neurofeedback training, whereas group 3 (n = 4) of Control patients underwent treatment as usual. Language performance and recovery were assessed using western aphasia battery and picture naming tasks, before and after the neurofeedback training.

TTK Chitra tilting disc heart valve model TC2: An assessment of fatigue life and durability.

Average age group of heart valve replacement in India and most of the Third World countries is below 30 years. Hence, the valve for such patients need to be designed to have a service life of 50 years or more which corresponds to 2000 million cycles of operation. The purpose of this study was to assess the structural performance of the TTK Chitra tilting disc heart valve model TC2 and thereby address its durability.

Brain-computer interfaces for neurorehabilitation.

Brain-computer interfaces (BCIs) enable control of computers and other assistive devices, such as neuro-prostheses, which are used for communication, movement restoration, neuro-modulation, and muscle stimulation, by using only signals measured directly from the brain. A BCI creates a new output channel for the brain to a computer or a device. This requires retrieval of signals of interest from the brain, and its use for neuro-rehabilitation by means of interfacing the signals to a computerized device.

Real-time functional MR imaging (fMRI) for presurgical evaluation of paediatric epilepsy.

Background: The role of fMRI in the presurgical evaluation of children with intractable epilepsy is being increasingly recognized. Real-time fMRI allows the clinician to visualize functional brain activation in real time. Since there is no off-line data analysis as in conventional fMRI, the overall time for the procedure is reduced, making it clinically feasible in a busy clinical sitting.