"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:de118076-3f38-4e40-a32a-03379aad26cc","http://resolver.tudelft.nl/uuid:de118076-3f38-4e40-a32a-03379aad26cc","Quantifying the nonlinear interaction in the nervous system based on phase-locked amplitude relationship","Yang, Y. (TU Delft Biomechatronics & Human-Machine Control); Yao, Jun (Northwestern University Feinberg School of Medicine); Dewald, Julius (Northwestern University Feinberg School of Medicine); van der Helm, F.C.T. (TU Delft Biomechatronics & Human-Machine Control); Schouten, A.C. (TU Delft Biomechatronics & Human-Machine Control)","","2020","This paper introduces the Cross-frequency Amplitude Transfer Function (CATF), a model-free method for quantifying nonlinear stimulus-response interaction based on phase-locked amplitude relationship. The CATF estimates the amplitude transfer from input frequencies at stimulation signal to their harmonics/intermodulation at the response signal. We first verified the performance of CATF in simulation tests with systems containing a static nonlinear function and a linear dynamic, i.e., Hammerstein and Wiener systems. We then applied the CATF to investigate the second-order nonlinear amplitude transfer in the human proprioceptive system from the periphery to the cortex. The simulation demonstrated that the CATF is a general method which can well quantify nonlinear stimulus-response amplitude transfer for different orders of nonlinearity in Wiener or Hammerstein system configurations. Applied to the human proprioceptive system, we found a complicated nonlinear system behavior with substantial amplitude transfer from the periphery stimulation to cortical response signals in the alpha band. This complicated system behavior may be associated with the nonlinear behavior of the muscle spindle and the dynamic interaction in the thalamocortical radiation. This paper provides a new tool to identify nonlinear interaction in the nervous system. The results provide novel insight of nonlinear dynamics in the human proprioceptive system","Cross-frequency Interaction; Nonlinear System; EEG; Nervous System; Human Proprioceptive System; Frequency Domain Analysis","en","journal article","","","","","","Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.","","2020-07-16","","","Biomechatronics & Human-Machine Control","","",""
"uuid:a2ec304d-0cd7-4d00-9664-963383c2f05b","http://resolver.tudelft.nl/uuid:a2ec304d-0cd7-4d00-9664-963383c2f05b","A novel approach for modeling neural responses to joint perturbations using the NARMAX method and a hierarchical neural network","Tian, Runfeng (Northwestern University); Yang, Y. (Northwestern University); van der Helm, F.C.T. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University); Dewald, J.P.A. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University)","","2018","The 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.","EEG; NARMAX; Neural modeling; Neural network; Non-linear system identification","en","journal article","","","","","","","","","","","Biomechatronics & Human-Machine Control","","",""
"uuid:4db1637e-554b-4c6e-9c92-99cab869ca3f","http://resolver.tudelft.nl/uuid:4db1637e-554b-4c6e-9c92-99cab869ca3f","Dynamic information flow based on EEG and diffusion MRI in stroke: A proof-of-principle study","Filatova, O.G. (TU Delft Biomechatronics & Human-Machine Control); Yang, Y. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University); Dewald, J.P.A. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University); Tian, Runfeng (Student TU Delft); Maceira-Elvira, Pablo (Swiss Federal Institute of Technology; Student TU Delft); Takeda, Yusuke (RIKEN Center for Emergent Matter Science (CEMS); ATR); Kwakkel, Gert (Amsterdam UMC); Yamashita, Okito (RIKEN Center for Emergent Matter Science (CEMS); ATR); van der Helm, F.C.T. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University)","","2018","In 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.","Brain dynamics; Diffusion MRI; EEG; Somatosensory evoked potentials (SEP); Stroke","en","journal article","","","","","","","","","","","Biomechatronics & Human-Machine Control","","",""
"uuid:4faa2692-c2b7-40dc-8b34-6f7ddb87f493","http://resolver.tudelft.nl/uuid:4faa2692-c2b7-40dc-8b34-6f7ddb87f493","Unveiling neural coupling within the sensorimotor system: directionality and nonlinearity","Yang, Y. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University); Dewald, J.P.A. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University); van der Helm, F.C.T. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University); Schouten, A.C. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University; University of Twente)","","2017","Neural coupling between the central nervous system and the periphery is essential for the neural control of movement. Corticomuscular coherence is a popular linear technique to assess synchronised oscillatory activity in the sensorimotor system. This oscillatory coupling originates from ascending somatosensory feedback and descending motor commands. However, corticomuscular coherence cannot separate this bidirectionality. Furthermore, the sensorimotor system is nonlinear, resulting in cross-frequency
coupling. Cross-frequency oscillations cannot be assessed nor exploited by linear measures. Here, we emphasise the need of novel coupling measures, which provide directionality and acknowledge nonlinearity, to unveil neural coupling in the sensorimotor system. We highlight recent advances in the field and argue that assessing directionality and nonlinearity of neural coupling
will break new ground in the study of the control of movement in healthy and neurologically impaired individuals.","corticomuscular interaction; cross-frequency coupling; granger causality; sensorimotor system; sensory feedback","en","journal article","","","","","","","","","","","Biomechatronics & Human-Machine Control","","",""