8 research outputs found
Brain Dynamic Information Flow Estimation Based on EEG and Diffusion MRI: A Proof-of-principle Study and Application in Stroke
No full textIn the hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Electroencephalography (EEG), with an excellent temporal resolution, can be used to reveal functional changes in the brain following a stroke. This study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which combines EEG, anatomical MRI and diffusion weighted imaging (DWI), to estimation brain dynamic information flow and its changes following a stroke. EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 88%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals, using matrices lateralization index and activation complexity. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.Mechanical Engineerin
A novel approach for modeling neural responses to joint perturbations using the NARMAX method and a hierarchical neural network
No full textThe human nervous system is an ensemble of connected neuronal networks. Modeling and system identification of the human nervous system helps us understand how the brain processes sensory input and controls responses at the systems level. This study aims to propose an advanced approach based on a hierarchical neural network and non-linear system identification method to model neural activity in the nervous system in response to an external somatosensory input. The proposed approach incorporates basic concepts of Non-linear AutoRegressive Moving Average Model with eXogenous input (NARMAX) and neural network to acknowledge non-linear closed-loop neural interactions. Different from the commonly used polynomial NARMAX method, the proposed approach replaced the polynomial non-linear terms with a hierarchical neural network. The hierarchical neural network is built based on known neuroanatomical connections and corresponding transmission delays in neural pathways. The proposed method is applied to an experimental dataset, where cortical activities from ten young able-bodied individuals are extracted from electroencephalographic signals while applying mechanical perturbations to their wrist joint. The results yielded by the proposed method were compared with those obtained by the polynomial NARMAX and Volterra methods, evaluated by the variance accounted for (VAF). Both the proposed and polynomial NARMAX methods yielded much better modeling results than the Volterra model. Furthermore, the proposed method modeled cortical responded with a mean VAF of 69.35% for a three-step ahead prediction, which is significantly better than the VAF from a polynomial NARMAX model (mean VAF 47.09%). This study provides a novel approach for precise modeling of cortical responses to sensory input. The results indicate that the incorporation of knowledge of neuroanatomical connections in building a realistic model greatly improves the performance of system identification of the human nervous system.Biomechatronics & Human-Machine Contro
Organic Thermoelectric Materials for Wearable Electronic Devices
No full textWearable electronic devices have emerged as a pivotal technology in healthcare and artificial intelligence robots. Among the materials that are employed in wearable electronic devices, organic thermoelectric materials possess great application potential due to their advantages such as flexibility, easy processing ability, no working noise, being self-powered, applicable in a wide range of scenarios, etc. However, compared with classic conductive materials and inorganic thermoelectric materials, the research on organic thermoelectric materials is still insufficient. In order to improve our understanding of the potential of organic thermoelectric materials in wearable electronic devices, this paper reviews the types of organic thermoelectric materials and composites, their assembly strategies, and their potential applications in wearable electronic devices. This review aims to guide new researchers and offer strategic insights into wearable electronic device development
Dynamic information flow based on EEG and diffusion MRI in stroke: A proof-of-principle study
No full textIn hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.Biomechatronics & Human-Machine Contro
Influence of heteroatoms on the charge mobility of anthracene derivatives
No full textThe introduction of polarizable heteroatom, such as O, and S, attached peripheral side chains of conjugated moieties such as polyacenes has not been systematically investigated. To study such effects, and to explore semiconductors with both high charge mobility and luminescence properties, we present a comparative systematic study of heteroatom effects on the conduction of organic semiconductors in a representative series of new organic semiconductors based on the blue phenyl-anthracene molecule core. Elucidated by the single-crystal X-ray analysis, thin film XRD and AFM measurements, a correlation between the molecular structure variation, film ordering, and charge mobility has been established. Quantum chemistry calculations combined with the Marcus-Hush electron transfer theory interpret the transport parameters. The anisotropic transport properties of these compounds were suggested by the DFT predictions and the high hole mobility in BEPAnt and BOPAnt is contributed mainly by the parallel packing of these compounds with the highest parallel to mu(h); these results are in good agreement with the experimental observations. Heteroatoms are demonstrated to influence the charge mobility dramatically. Our systematic investigation will provide valuable guidance for a judicious material design of semiconductors for OTFT applications.Shenzhen Key Laboratory of Shenzhen Science and Technology Plan [ZDSYS20140509094114164]; Guangdong Talents Project, NSFC [51373075]; National Basic Research Program of China (973 Program) [2015CB856505, 2015CB932200]; National Research Foundation [20133221 110004]; Shenzhen Peacock Progra [KQTD2014062714543296]; Provincial Science and technology project of Guangdong Province [2015B090914002, 2014B090914003]SCI(E)[email protected]; [email protected]
