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. METHODS: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. RESULTS: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. CONCLUSION: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. SIGNIFICANCE: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks.

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. We first extracted the spatiotemporal features of EMG energy and frequency maps to improve learning efficiency, given that EMG signals are intrinsically stochastic. We then established a generic neural network model by training on the populational neuron firing activities of multiple participants. Using a regression model, we continuously predicted individual finger forces in real-time. We compared the force prediction performance with two state-of-the-art approaches: a neuron-decomposition method and a classic EMG-amplitude method. RESULTS: Our results showed that the generic CNN model outperformed the subject-specific neuron-decomposition method and the EMG-amplitude method, as demonstrated by a higher correlation coefficient between the measured and predicted forces, and a lower force prediction error. In addition, the CNN model revealed more stable force prediction performance over time. CONCLUSIONS: Overall, our approach provides a generic and efficient continuous neural decoding approach for real-time and robust human-robot interactions.

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). Object orientation and object width are two important parameters for controlling the wrist angle and the grasp aperture of the assistive device to replicate a human-like stable grasp. Vision systems are already evolved to measure the geometrical attributes of the object to control the grasp with a prosthetic hand. However, most of the existing vision systems are integrated with electromyography and require some amount of voluntary muscle movement to control the vision system. Due to that reason, those systems are not beneficial for the users with brain-controlled assistive devices. Here, we implemented a vision system which can be controlled through the human gaze. We measured the vertical and horizontal electrooculogram signals and controlled the pan and tilt of a cap-mounted webcam to keep the object of interest in focus and at the centre of the picture. A simple 'signature' extraction procedure was also utilized to reduce the algorithmic complexity and system storage capacity. The developed device has been tested with ten healthy participants. We approximated the object orientation and the size of the object and determined an appropriate wrist orientation angle and the grasp aperture size within 22 ms. The combined accuracy exceeded 75%. The integration of the proposed system with the brain-controlled grasp assistive device and increasing the number of grasps can offer more natural manoeuvring in grasp for ALS patients.

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. Generalization of grasp patterns should be explored without any prior knowledge of the objects. In this regard, the similarity of motor imagery for different objects requiring similar grasp pattern can be utilized. It is also necessary to identify the brain regions that exhibit prominent distinguishability during different grasp patterns. In this study, we propose a chaos-based method to decode the motor imagery of two quite similar Power grasp patterns-cylindrical and spherical-for holding various objects. Three distinct suitable objects were chosen for each of the two patterns, and a 29-channel EEG was taken of 18 healthy participants to explore motor imagery for grasping the objects. Nonlinear correlation dimension was employed on the EEG data, at sub-band levels α, upper ß, and γ, to analyse the distinguishability, as well as the similarity of grasp patterns for the objects. ANOVA was subsequently performed on the obtained CD parameters to identify the contribution of each electrode channel. Furthermore, using an SVM classifier, more than 80% accuracy was obtained in classifying the grasping patterns at the upper ß sub-band. The outcome may lead to identification of optimum feature sets of motor imagery from specific brain regions for random objects grasps.

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). METHODS: The study design is a cross-sectional analytical survey. The study setting was (1) Hospital-based survey of patients with CHD and their parents. (2) Community survey of controls and their parents. Subjects included (1) Children/adolescents with CHD between the ages of 2 years and 18 years and their parents enrolled in a previous study (n=308). (2) Unmatched community controls (719 children/adolescents, aged 2-18 years) and their parents. Participants were given PedsQL 4.0 to fill out details. Parents assisted children 5-7 years of age in filling the questionnaires. Children younger than 5 years had only parent-reported HRQOL and those above 5 years had both self-reported and parent-reported HRQOL. RESULTS: The median (IQR) total generic HRQOL from self-reports for CHD subjects and controls were 71.7 (62.0, 84.8) and 91.3 (82.6, 95.7), respectively. The corresponding figures for parent-reports were 78.3 (63.0, 90.5) and 92.4 (87.0, 95.7) respectively. The adjusted median difference was -20.6 (99% CI -24.9 to -16.3, p<0.001) for self-reported and -14.1 (99% CI -16.7 to -11.6, p<0.001) for parent-reported total HRQOL between patients with CHD and controls. Cardiac-specific HRQOL by self-reports was 75.0 (53.6, 92.9) for heart problems, 95.0 (73.8, 100.0) for treatment barriers, 83.3 (66.7, 100.0) for physical appearance, 87.5 (62.5, 100.0) for treatment-related anxiety, 91.7 (68.8, 100.0) for cognitive problems and 83.3 (66.7, 100.0) for communication. The values for parent-reports were 71.4 (53.6, 85.7), 100.0 (75.0, 100.0), 100.0 (75.0, 100.0), 81.3 (50.0, 100.0), 100.0 (81.2, 100.0) and 83.3 (50.0, 100.0), respectively. CONCLUSIONS: Children and adolescents with uncorrected CHD reported significant reductions in overall quality of life compared with controls.

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. Research on speech restoration through brain computer interface (BCI) is still at an early stage. Through this study, we have explored the regression between the chaos parameters of acoustic signal and electroencephalography (EEG) signal for different vowels chosen from International Phonetic Alphabets (IPA). The vowels were categorised into two categories, namely, soft vowels and diphthongs. We have selected the EEG channels based on their higher contribution towards the first principal component. Goodness of fit parameters were evaluated for the regression analysis to explore the most suitable chaos parameter.

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. Ensemble learning is very effective in this context to achieve higher accuracy in Brain-Computer Interface (BCI) domain. In this study, we have proposed enhanced classification algorithms to achieve higher classification accuracies. The methods were evaluated against the motor imagery data from Dataset 2a of the publicly available BCI Competition IV (2008). This dataset consists of 22 channels EEG data of 9 subjects for four different movements. A tree based ensemble approach for supervised classification, Extra-Trees algorithm, has been proposed in this paper and also evaluated for its efficacy on this dataset to classify between left hand and right hand movement imaginations. Moreover, this classifier has its inherent capability to select optimum features. Furthermore, in this paper an extension of the binary classification into multiclass domain is also implemented with error correcting output codes (ECOC) approach using the same dataset. Subject-specific frequency bands $\alpha$ (8-12Hz) and $\beta$ (12-30Hz) along with $HG$ (70-100Hz) were considered to extract CSP features. We have achieved an individual peak accuracy of 98È¥ and 84È¥ in binary class and multiclass classification respectively. Furthermore, the results yielded a mean kappa value of 0.58 across all the subjects. This kappa value is higher than of the winner of competition and also from the most of the other approaches applied in this dataset.

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. Through this study, we have explored significance of multifractal parameters for different imagined vowels chosen from International Phonetic Alphabets (IPA). The vowels were categorised into two categories, namely, soft vowels and diphthongs. Multifractal analysis at EEG subband levels were evaluated. We have also reported significant contrasts between spatiotemporal distributions with fractal analysis for activation of different brain regions in imagining vowels.

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. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with "one against one" multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future. The model to virtualise motor imagery based fingertip force prediction with inherent slippage correction for different grasp types ᅟ.

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. SETTING: We collected HRQOL data on infants and toddlers through a community survey for controls and through a hospital-based survey for those with CHD. PATIENTS: A total of 499 subjects with confirmed CHD in the age group of 1-24 months admitted for elective surgery in the study institution were selected by consecutive sampling. In addition, we selected 628 control children in the same age group from an area within the 10 km radius of the study institution. MAIN OUTCOME MEASURES: The data contain parent proxy report of HRQOL. The Pediatric Quality of Life Inventory (PedsQL 4.0) was used to collect HRQOL data. Questionnaires were self-administered for parents. RESULTS: The mean total proxy HRQOL scores were significantly higher in control infants compared with infants with CHD (adjusted mean difference 5.0, 99% CI 2.5 to 7.5, p<0.001). Corresponding figure for toddlers was 7.6 (95% CI 5.0 to 10.2, p<0.001). There was no significant difference in total HRQOL scores across CHD functional classes among infants and toddlers (p=0.212 and p=0.502, respectively). CONCLUSIONS: Infants and toddlers with uncorrected CHD have significant deficiency in proxy HRQOL compared with their controls. The functional class of CHD appears to have no differential impact on overall HRQOL deficiency in this age group.

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. A total of 719 children/adolescents in the age group of 2-18 yr were enrolled using stratified random cluster sampling. A total of 40 clusters (cluster size 18) were selected for the study. The data contained child self-report and parent proxy report from healthy children and their parents/caretakers. The Pediatric Quality of Life Inventory 4.0 (PedsQL4.0) Generic Core Scale was used to collect HRQOL data. Questionnaires were self-administered for parents and children aged 8-18 yr. In the age group of five to seven years, parents assisted the children in filling questionnaires. RESULTS: The mean HRQOL total scores from child self-report and parent proxy report were 87.50±11.10 and 90.10±9.50 respectively, for children aged 2-18 yr. Social functioning had the highest scores and emotional functioning had the lowest scores for the entire sample and subgroups. The mean values for HRQOL in the current study were significantly different from the reference study for both child (87.39 vs. 83.91, P<0.001) and parent proxy reports (90.03 vs. 82.29, P<0.001) when compared between children aged 2-16 yr. INTERPRETATION & CONCLUSIONS: The study provided reference values for HRQOL in healthy children and adolescents from Kerala, India, that appeared to be different from existing international reference. Similar studies need to be done in different parts of India to generate a country-specific HRQOL reference for Indian children.

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. We also explored how distressed mothers interact with their families and the wider community. METHODS: Participants were identified during a cluster-randomised controlled trial in which mothers were screened for psychological distress using the 12-item General Health Questionnaire (GHQ-12). We conducted 22 semi-structured interviews with distressed mothers (GHQ-12 score ≥ 5) and one with a traditional healer (dhami), as well as 12 focus group discussions with community members. Data were analysed using grounded theory methods and a model was developed to explain psychological distress in this setting. RESULTS: We found that distress was termed tension by participants and mainly described in terms of physical symptoms. Key perceived causes of distress were poor health, lack of sons, and fertility problems. Tension developed in a context of limited autonomy for women and perceived duty towards the family. Distressed mothers discussed several strategies to alleviate tension, including seeking treatment for perceived physical health problems and tension from doctors or dhamis, having repeated pregnancies until a son was delivered, manipulating social circumstances in the household, and deciding to accept their fate. Their ability to implement these strategies depended on whether they were able to negotiate with their in-laws or husbands for resources. CONCLUSIONS: Vulnerability, as a consequence of gender and social disadvantage, manifests as psychological distress among mothers in Dhanusha. Screening tools incorporating physical symptoms of tension should be envisaged, along with interventions to address gender inequity, support marital relationships, and improve access to perinatal healthcare.