Concurrent and Continuous Prediction of Finger Kinetics and Kinematics via Motoneuron Activities.

Objective: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers.

A generic neural network model to estimate populational neural activity for robust neural decoding.

Background: Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel generic neural network model that can continuously predict finger forces based on decoded populational motoneuron firing activities.

Method: We implemented convolutional neural networks (CNNs) to learn the mapping from high-density electromyogram (HD-EMG) signals of forearm muscles to populational motoneuron firing frequency.

An Electro-Oculogram Based Vision System for Grasp Assistive Devices-A Proof of Concept Study.

Humans typically fixate on objects before moving their arm to grasp the object. Patients with ALS disorder can also select the object with their intact eye movement, but are unable to move their limb due to the loss of voluntary muscle control. Though several research works have already achieved success in generating the correct grasp type from their brain measurement, we are still searching for fine controll over an object with a grasp assistive device (orthosis/exoskeleton/robotic arm).

Chaotic behaviour of EEG responses with an identical grasp posture.

Individuals with severe neuromuscular ailments can benefit from restoring their grasp activities with a brain-controlled upper-limb neuroprosthesis. EEG signals can be utilized as the driving source, and to implement natural human-like grasping abilities. Although good accuracy has already been achieved in classifying the various grasp patterns for specific sets of objects, unseen objects are still a hurdle in real-life implementation.

Regression between EEG and Speech Signals for Spoken Vowels.

One of the primary difference of mankind from other species is his ability to communicate verbally. Our brain upon framing a sentence, coordinates with the oro-pharyngeal-laryngeal muscle groups to produce the speech with the help of vocal cord and mouth aperture. However, some individuals due to congenital or illness, may loose their ability to speak in spite of their brain framing speech.

Health-related quality of life (HRQOL) in children and adolescents with congenital heart disease: a cross-sectional survey from South India.

Objective: There are limited data on health-related quality of life (HRQOL) for children and adolescents with uncorrected congenital heart disease (CHD) from low-income and middle-income countries where late presentation is common. We sought to compare HRQOL of children and adolescents with uncorrected CHD to that of controls using the Pediatric Quality of Life Inventory (PedsQL 4.0).

A Randomised Ensemble Learning Approach for Multiclass Motor Imagery Classification Using Error Correcting Output Coding.

Common Spectral Pattern (CSP) algorithm remains predominant for feature extraction from multichannel EEG motor imagery data. However, multiclass classification of from this featureset has been a challenging job. Different approaches have been proposed to be applied on featureset of different EEG subbands to achieve significant classification accuracy.

Multifractal Analysis of Speech Imagery of IPA Vowels.

In Brain Computer Interfacing (BCI), speech imagery is still at nascent stage of development. There are few studies reported considering mostly vowels or monosyllabic words. However, language specific vowels or words made it harder to standardise the whole analysis of electroencephalography (EEG) while distinguishing between them.

A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG.

A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures.

Health-related quality of life in Indian children: A community-based cross-sectional survey.

Background & Objectives: There are limited data on health-related quality of life (HRQOL) related to Indian children. The objective of this study was to construct a generic HRQOL reference for children aged 2-18 yr from a community setting.

Methods: The study was a community-based cross-sectional survey.

Health-related quality of life in infants and toddlers with congenital heart disease: a cross-sectional survey from South India.

Objective: There are limited data on health-related quality of life (HRQOL) for infants and toddlers with congenital heart disease (CHD). We sought to compare generic HRQOL of infants and toddlers between CHD subjects and controls.

Design: Dual-setting, cross-sectional analytical survey.

Understanding psychological distress among mothers in rural Nepal: a qualitative grounded theory exploration.

Background: There is a large burden of psychological distress in low and middle-income countries, and culturally relevant interventions must be developed to address it. This requires an understanding of how distress is experienced. We conducted a qualitative grounded theory study to understand how mothers experience and manage distress in Dhanusha, a low-resource setting in rural Nepal.