Rihui completed his PhD in Biomedical Engineering at the University of Houston. His PhD work primarily focused on applying multimodal brain imaging techniques (EEG and fNIRS) to explore and characterize brain dynamics associated with various brain disorders (Alzheimer's Disease and Stroke). Currently, Rihui is involved in several projects: 1) investigating dynamic functional connectivity and brain states during social interactions using fNIRS-based hyperscanning technique; 2) developing multimodal approaches to characterize the brain alterations in patients with Fragile X and establish robust risk-predictive models for the early management of Fragile X Syndrome.
Honors & Awards
Graduate Tuition Fellowship, University of Houston (2015-2020)
Presidential Fellowship, University of Houston (2015-2017)
Postdoctoral Fellowship, Center for Interdisciplinary Brain Sciences Research, Division of Brain Sciences, School of Medicine, Stanford University (2020)
Ph.D., University of Houston, Biomedical Engineering (2020)
M.S., Sun Yat-sen University, Biomedical Engineering (2015)
B.E., Sun Yat-sen University, Biomedical Engineering (2012)
Allan Reiss, Postdoctoral Faculty Sponsor
Multimodal Neuroimaging Using Concurrent EEG/fNIRS for Poststroke Recovery Assessment: An Exploratory Study.
Neurorehabilitation and neural repair
BACKGROUND: Persistent motor deficits are very common in poststroke survivors and often lead to disability. Current clinical measures for profiling motor impairment and assessing poststroke recovery are largely subjective and lack precision.OBJECTIVE: A multimodal neuroimaging approach was developed based on concurrent functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to identify biomarkers associated with motor function recovery and document the poststroke cortical reorganization.METHODS: EEG and fNIRS data were simultaneously recorded from 9 healthy controls and 18 stroke patients during a hand-clenching task. A novel fNIRS-informed EEG source imaging approach was developed to estimate cortical activity and functional connectivity. Subsequently, graph theory analysis was performed to identify network features for monitoring and predicting motor function recovery during a 4-week intervention.RESULTS: The task-evoked strength at ipsilesional primary somatosensory cortex was significantly lower in stroke patients compared with healthy controls (P < .001). In addition, across the 4-week rehabilitation intervention, the strength at ipsilesional premotor cortex (PMC) (R = 0.895, P = .006) and the connectivity between bilateral primary motor cortices (M1) (R = 0.9, P = .007) increased in parallel with the improvement of motor function. Furthermore, a higher baseline strength at ipsilesional PMC was associated with a better motor function recovery (R = 0.768, P = .007), while a higher baseline connectivity between ipsilesional supplementary motor cortex (SMA)-M1 implied a worse motor function recovery (R = -0.745, P = .009).CONCLUSION: The proposed multimodal EEG/fNIRS technique demonstrates a preliminary potential for monitoring and predicting poststroke motor recovery. We expect such findings can be further validated in future study.
View details for DOI 10.1177/1545968320969937
View details for PubMedID 33190571
An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease
JOURNAL OF NEUROSCIENCE METHODS
2020; 336: 108618
Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications.Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance.The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31 %) by integrating their complementary properties, compared to using EEG (65.52 %) or fNIRS alone (58.62 %). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD.Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population.These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.
View details for DOI 10.1016/j.jneumeth.2020.108618
View details for Web of Science ID 000526107700003
View details for PubMedID 32045572
View details for PubMedCentralID PMC7376762
Enhancing fNIRS Analysis Using EEG Rhythmic Signatures: an EEG-informed fNIRS Analysis Study.
IEEE transactions on bio-medical engineering
Neurovascular coupling represents the relationship between changes in neuronal activity and cerebral hemodynamics. Concurrent Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recording and integration analysis has emerged as a promising multi-modal neuroimaging approach to study the neurovascular coupling as it provides complementary properties with regard to high temporal and moderate spatial resolution of brain activity. In this study we developed an EEG-informed-fNIRS analysis framework to investigate the neuro-correlate between neuronal activity and cerebral hemodynamics by identifying specific EEG rhythmic modulations which contribute to the improvement of the fNIRS-based general linear model (GLM) analysis. Specifically, frequency-specific regressors derived from EEG were used to construct design matrices to guide the GLM analysis of the fNIRS signals collected during a hand grasp task. Our results showed that the EEG-informed fNIRS GLM analysis, especially the alpha and beta band, revealed significantly higher sensitivity and specificity in localizing the task-evoked regions compared to the canonical boxcar model, demonstrating the strong correlations between hemodynamic response and EEG rhythmic modulations. Results also indicated that analysis based on the deoxygenated hemoglobin (HbR) signal slightly outperformed the oxygenated hemoglobin (HbO)-based analysis. The findings in our study not only validate the feasibility of enhancing fNIRS GLM analysis using simultaneously recorded EEG signals, but also provide a new perspective to study the neurovascular coupling of brain activity.
View details for DOI 10.1109/TBME.2020.2971679
View details for PubMedID 32031925
Functional Network Alterations in Patients With Amnestic Mild Cognitive Impairment Characterized Using Functional Near-Infrared Spectroscopy
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
2020; 28 (1): 123–32
Amnestic mild cognitive impairment (aMCI) is conceptualized as a cognitive disorder characterized by memory deficits. Patients with aMCI are treated as prodromal stage of Alzheimer's disease (AD) and have an increased likelihood of developing into AD. The investigation of aMCI is therefore fundamental to the early detection and intervention of AD. Growing evidence has shown that functional network alterations induced by cognition impairment can be captured by advanced neuroimaging techniques. In this study, functional near-infrared spectroscopy (fNIRS), an affordable, robust and portable neuroimaging modality, was employed to characterize the functional network in aMCI patients. FNIRS data were collected from 16 healthy controls and 16 aMCI patients using a digits verbal span task. Functional networks were constructed from temporal hemodynamic response signals. Graph-based indices were then calculated from the constructed brain networks to assess global and regional differences between the groups. Results suggested that brain networks in aMCI patients were characterized with higher integration as well as higher segregation compared to healthy controls. In addition, major regions of interest (ROIs) within frontal, temporal, precentral and parietal areas were identified to be associated with cognition impairment. Our findings validate the feasibility of utilizing fNIRS as a portable and reliable tool for the investigation of abnormal network alterations in patients with cognition decline.
View details for DOI 10.1109/TNSRE.2019.2956464
View details for Web of Science ID 000508375400013
View details for PubMedID 31804939
Cortical Hemodynamic Response and Connectivity Modulated by Sub-threshold High-Frequency Repetitive Transcranial Magnetic Stimulation
FRONTIERS IN HUMAN NEUROSCIENCE
2019; 13: 90
Repetitive transcranial magnetic stimulation (rTMS) at sub-threshold intensity is a viable clinical strategy to enhance the sensory and motor functions of extremities by increasing or decreasing motor cortical excitability. Despite this, it remains unclear how sub-threshold rTMS modulates brain cortical excitability and connectivity. In this study, we applied functional near-infrared spectroscopy (fNIRS) to investigate the alterations in hemodynamic responses and cortical connectivity patterns that are induced by high-frequency rTMS at a sub-threshold intensity. Forty high-frequency (10 Hz) trains of rTMS at 90% resting motor threshold (RMT) were delivered through a TMS coil placed over 1-2 cm lateral from the vertex. fNIRS signals were acquired from the frontal and bilateral motor areas in healthy volunteers (n = 20) during rTMS administration and at rest. A significant reduction in oxygenated hemoglobin (HbO) concentration was observed in most defined regions of interest (ROIs) during the stimulation period (p < 0.05). Decreased functional connectivity within prefrontal areas as well as between symmetrical ROI-pairs was also observed in most participants during the stimulation (p < 0.05). Results suggest that fNIRS imaging is able to provide a reliable measure of regional cortical brain activation that advances our understanding of the manner in which sub-threshold rTMS affects cortical excitability and brain connectivity.
View details for DOI 10.3389/fnhum.2019.00090
View details for Web of Science ID 000461778900001
View details for PubMedID 30941025
View details for PubMedCentralID PMC6434517
Dynamic cortical connectivity alterations associated with Alzheimer's disease: An EEG and fNIRS integration study
2019; 21: 101622
Emerging evidence indicates that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain network. Exploring alterations in the AD brain network is therefore of great importance for understanding and treating the disease. This study employs an integrative functional near-infrared spectroscopy (fNIRS) - electroencephalography (EEG) analysis approach to explore dynamic, regional alterations in the AD-linked brain network. FNIRS and EEG data were simultaneously recorded from 14 participants (8 healthy controls and 6 patients with mild AD) during a digit verbal span task (DVST). FNIRS-based spatial constraints were used as priors for EEG source localization. Graph-based indices were then calculated from the reconstructed EEG sources to assess regional differences between the groups. Results show that patients with mild AD revealed weaker and suppressed cortical connectivity in the high alpha band and in beta band to the orbitofrontal and parietal regions. AD-induced brain networks, compared to the networks of age-matched healthy controls, were mainly characterized by lower degree, clustering coefficient at the frontal pole and medial orbitofrontal across all frequency ranges. Additionally, the AD group also consistently showed higher index values for these graph-based indices at the superior temporal sulcus. These findings not only validate the feasibility of utilizing the proposed integrated EEG-fNIRS analysis to better understand the spatiotemporal dynamics of brain activity, but also contribute to the development of network-based approaches for understanding the mechanisms that underlie the progression of AD.
View details for DOI 10.1016/j.nicl.2018.101622
View details for Web of Science ID 000460337700035
View details for PubMedID 30527906
View details for PubMedCentralID PMC6411655
Early Detection of Alzheimer's Disease Using Non-invasive Near-Infrared Spectroscopy
FRONTIERS IN AGING NEUROSCIENCE
2018; 10: 366
Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, wherein patients have an increased likelihood of developing Alzheimer's disease (AD). The classification of MCI and different AD stages is therefore fundamental for understanding and treating the disease. This study aimed to comprehensively investigate the hemodynamic response patterns among various subject groups. Functional near-infrared spectroscopy (fNIRS) was employed to measure signals from the frontal and bilateral parietal cortices of healthy controls (n = 8), patients with MCI (n = 9), mild (n = 6), and moderate/severe AD (n = 7) during a digit verbal span task (DVST). The concentration changes of oxygenated hemoglobin (HbO) in various subject groups were thoroughly explored and tested. Result revealed that abnormal patterns of hemodynamic response were observed across all subject groups. Greater and steeper reductions in HbO concentration were consistently observed across all regions of interest (ROIs) as disease severity developed from MCI to moderate/severe AD. Furthermore, all the fNIRS-derived indexes were found to be significantly and positively correlated to the clinical scores in all ROIs (R ≥ 0.4, P < 0.05). These findings demonstrate the feasibility of utilizing fNIRS for the early detection of AD, suggesting that fNIRS-based approaches hold great promise for exploring the mechanisms underlying the progression of AD.
View details for DOI 10.3389/fnagi.2018.00366
View details for Web of Science ID 000449661300001
View details for PubMedID 30473662
View details for PubMedCentralID PMC6237862
Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features
FRONTIERS IN HUMAN NEUROSCIENCE
2017; 11: 462
Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties. In this study, we aimed to investigate whether the early temporal information extracted from singular EEG and fNIRS channels on each hemisphere can be used to enhance the accuracy and efficiency of a hybrid EEG-fNIRS BCI system. Eleven healthy volunteers were recruited and underwent simultaneous EEG-fNIRS recording during a motor execution task that included left and right hand movements. Singular EEG and fNIRS channels corresponding to the motor cortices of each hemisphere were selected using a general linear model. Early temporal information was extracted from the EEG channel (0-1 s) along with initial hemodynamic dip information from fNIRS (0-2 s) for classification using a support vector machine (SVM). Results demonstrated a lofty classification accuracy using a minimal number of channels and features derived from early temporal information. In conclusion, a hybrid EEG-fNIRS BCI system can achieve higher classification accuracy (91.02 ± 4.08%) and efficiency by integrating their complimentary properties, compared to using EEG (85.64 ± 7.4%) or fNIRS alone (85.55 ± 10.72%). Such a hybrid system can also achieve minimal response lag in application by focusing on rapidly-evolving brain dynamics.
View details for DOI 10.3339/fnhum.2017.00462
View details for Web of Science ID 000412011200002
View details for PubMedID 28966581
View details for PubMedCentralID PMC5605645
Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abundant in real applications. Although the semi-supervised learning (SSL) allows us to utilize both labeled and unlabeled data to improve the classification performance as against supervised algorithms, it has been reported that unlabeled data occasionally undermine the performance of SSL in some cases. To overcome this challenge, we propose a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. Specifically, the ELM model is firstly used to predict unlabeled samples and then the collaborative representation (CR) approach is employed to reconstruct the unlabeled samples according to the obtained prediction results, from which the risk degree of unlabeled sample is defined. A risk-based regularization term is then constructed accordingly and embedded into the objective function of the SS-ELM. Experiments conducted on benchmark and EEG datasets demonstrate that the proposed method outperforms the ELM and SS-ELM algorithm. Moreover, the proposed CR-SSELM even offers the best performance while SS-ELM yields worse performance compared with its supervised counterpart (ELM). Graphical abstract This paper proposes a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. It is aim to solve the safety problem of SS-ELM method that SS-ELM yields worse performance than ELM. With the help of safety mechanism, the performance of our method is still better than supervised ELM method.
View details for DOI 10.1007/s11517-020-02227-4
View details for Web of Science ID 000549244900001
View details for PubMedID 32676841
Altered Muscle Networks in Post-Stroke Survivors.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
2020; 2020: 3771–74
Muscle networks represent a series of interactions among muscles in the central nervous system's effort to reduce the redundancy of the musculoskeletal system in motor-control. How this occurs has only been investigated recently in healthy subjects with a novel technique exploring the functional connectivity between muscles through intermuscular coherence (IMC), yet the potential value of this method in characterizing the alteration of muscular networks after stroke remains unknown. In this study, muscle networks were assessed in post-stroke survivors and healthy controls to identify possible alterations in the neural oscillatory drive to muscles after stroke. Surface electromyography (sEMG) was collected from eight key upper extremity muscles to non-invasively determine the common neural input to the spinal motor neurons innervating muscle fibers. Coherence was computed between all possible muscle pairs and further decomposed by non-negative matrix factorization (NMF) to identify the common spectral patterns of coherence underlying the muscle networks. Results suggested that the number of identified muscle networks during dynamic force generation decreased after stroke. The findings in this study could provide a new prospective for understanding the motor control recovery during post-stroke rehabilitation.
View details for DOI 10.1109/EMBC44109.2020.9176646
View details for PubMedID 33018822
- Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study ELECTRONICS 2020; 9 (5)
Establishing functional brain networks using a nonlinear partial directed coherence method to predict epileptic seizures
JOURNAL OF NEUROSCIENCE METHODS
2020; 329: 108447
Epilepsy is a neurological disorder characterized by unpredictable seizures that can lead to severe health problems. EEG techniques have shown to be advantageous for studying and predicting epileptic seizures, thanks to their cost-effectiveness, non-invasiveness, portability and the capability for long-term monitoring. Linear and non-linear EEG analysis methods have been developed for the effective prediction of seizure onset, however both methods remain blind to underlying alterations of the structural and functional brain networks associated with epileptic seizures. Such information is employed in this study to develop novel method for epileptic seizure prediction.In this study, nonlinear partial directed coherence (NPDC) was employed as measure of functional brain networks (FBNs) and analyzed to reveal the directional flow of epilepsy-linked brain activity. A novel prediction strategy was then developed for the prediction of epileptic seizures by introducing extracted network features to an extreme learning machine (ELM).Results show that the proposed method achieved favorable performance across all subjects and in all EEG frequency bands, with best accuracy of 89.2% in beta band and an optimal prediction time of 1356.4 s in delta bands, which outperforms currently available approaches.Our NPDC based on FBNs methods approach surpasses the accuracy of pure graph theory and pure non-linear methods with a significantly increased prediction time.The findings of this study demonstrate that the proposed prediction strategy is suitable for the prediction of seizure onset.
View details for DOI 10.1016/j.jneumeth.2019.108447
View details for Web of Science ID 000499768800009
View details for PubMedID 31614163
Driving Fatigue Detection from EEG Using a Modified PCANet Method
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
2019; 2019: 4721863
The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.
View details for DOI 10.1155/2019/4721863
View details for Web of Science ID 000477826300001
View details for PubMedID 31396270
View details for PubMedCentralID PMC6664732
Emergency glioma resection but not hours of operation predicts perioperative complications: A single center study
CLINICAL NEUROLOGY AND NEUROSURGERY
2019; 182: 11–16
Physical and mental status of neurosurgeons may vary with emergency status and hours of operation, which may impact the outcome of patients undergoing surgery. This study aims to clarify the influence of these parameters on outcome after surgery in glioma patients.A total of 477 nonemergency surgery (NES) and 30 emergency surgery (ES) were enrolled in this study. Using propensity score matching (PSM) analysis, 97 pairs of procedures from NES group were generated and then classified as group M (morning procedures, 8:00 a.m-1:00 p.m) or group A (afternoon or night procedures, 1:00 p.m-8:00 p.m). 30 emergency procedures were classified into group ESa (daytime emergency surgery, 8:00 a.m-6:00 p.m) and group ESb (nighttime surgery procedures, 6:00 p.m-8:00 a.m the next day). Differences in intraoperative risk factors and postoperative complications were analyzed.Postoperative complications, including death within 30 days (p = 0.004), neurological function deficit (p = 0.012), systemic infection (p < 0.001) were significant higher in emergency procedures. Intraoperative risk factors including blood loss (p < 0.001), blood transfusion (p = 0.036) were also higher in emergency procedures than nonemergency procedures, although both procedures had comparable time duration (p = 0.337). By PSM analysis, patients in group M and group A were well matched and no significant difference of intraoperative risk factors and postoperative complications (all p > 0.05) were found. Furthermore, incidence of intraoperative risk factors and postoperative complications were similar in both groups ESa and ESb (all p > 0.05).Emergency glioma resection is a very important risk factors of perioperative mortality and morbidity for patients. However, hours of operation did not necessarily predict postoperative mortality or morbidity, either in emergency or nonemergency glioma resection.
View details for DOI 10.1016/j.clineuro.2019.04.010
View details for Web of Science ID 000472812800003
View details for PubMedID 31054423
Synchronous analysis of brain regions based on multi-scale permutation transfer entropy
COMPUTERS IN BIOLOGY AND MEDICINE
2019; 109: 272–79
The coupling of electroencephalographic (EEG) signals reflects the interaction between brain regions, which is of great importance for the assessment of motor function in post-stroke patients. In this study, the measurement of multi-scale permutation transfer entropy (MPTE) was presented and employed to characterize the coupling between the EEG signals measured from the bilateral motor and sensory areas. Post-stroke patients (n = 5) and healthy volunteers (n = 6) were recruited and participated in a hand grip task with different levels of contraction. MPTE values were computed and analyzed across various frequency bands for all subjects. Results showed that, for healthy controls, the coupling between motor and sensory areas was bi-directional and tended to be strongest in beta band. In particular, greater beta-band MPTE was found in the dominant hand and coupling strength decreased as contraction strength increased. Additionally, coupling between the motor and sensory areas of stroke patients exhibited weaker beta-band MPTE than that of healthy controls. Findings suggest that MPTE is able to quantitatively characterize the coupling properties between multiple brain regions, providing a promising approach to study the underlying mechanisms of functional motor recovery.
View details for DOI 10.1016/j.compbiomed.2019.04.038
View details for Web of Science ID 000472590500028
View details for PubMedID 31096091
Analysis of measurement electrode location in bladder urine monitoring using electrical impedance
BIOMEDICAL ENGINEERING ONLINE
2019; 18: 34
The aim of this study was to document more appropriate electrode location of a four-electrode-based electrical impedance technology in the monitoring of bladder filling, and to characterize the relationship between bladder filling duration and the measured electrical impedances.A simulation study, based on a 2-dimension computational model, was conducted to determine the preferable locations of excitation and measurement electrodes in a conventional four-electrode setup. A human observation study was subsequently performed on eight healthy volunteers during natural bladder urine accumulation to validate the result of the simulation study. The correlation between the bladder filling time and the measured electrical impedance values was evaluated.The preferable location of measurement electrodes was successively validated by the model simulation study and human observation study. Result obtained via the selected electrodes location revealed a significant negative correlation (R = 0.916 ± 0.059, P < 0.001) between the measured electrical impedance and the urine accumulation time, which was consistent with the result of simulation study.The findings in this study not only documented the desirable electrodes location to monitor the process of bladder urine accumulation using four-electrode measurement, but also validated the feasibility of utilizing electrical impedance technique to monitor and estimate the bladder urine volume for those with urological disorders.
View details for DOI 10.1186/s12938-019-0651-4
View details for Web of Science ID 000462195800002
View details for PubMedID 30902056
View details for PubMedCentralID PMC6431015
EEG-based brain network analysis in stroke patients during a motor execution task
IEEE. 2019: 887–90
View details for Web of Science ID 000469933200216
Graph-based Brain Network Analysis in Epilepsy: an EEG Study
IEEE. 2019: 130–33
View details for Web of Science ID 000469933200033
Frequency-dependent anisotropic modeling and analysis using mfEIT: A computer simulation study
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING
2018; 34 (7): e2980
Electrical properties of human tissues are usually linked with structure of thin insulating membranes and thereby reflect physiological function of the tissues or organs. It is clinically important to characterize electrical properties of tissues in vivo. Electrical impedance tomography is a recently developed medical imaging technique, which has been exploited to characterize electrical properties (conductivity and permittivity) of human tissues by injecting currents and measuring the resulting voltages at boundary electrodes. The electrical characteristic of a majority of human tissues, such as bones, muscles, and brain white matter, exhibits an anisotropic property. The anisotropic phenomenon of human tissues is frequency dependent that vanishes at high frequencies. Previous electrical impedance tomography studies that aimed at the reconstruction of anisotropic subject tissues have been focused on the theoretical analysis of uniqueness up to a diffeomorphism or the establishment of an accurate forward model by using an anisotropic conductivity tensor. However, effects of the current frequency on the accuracy of the reconstructions of anisotropic subjects remain poorly studied. The goal of this study is to examine the feasibility of multifrequency electrical impedance tomography by using it in a simulation study to recover the frequency-dependent anisotropic properties of a phantom subject composed of alternating insulating and conductive layers. The anisotropic properties of the subject were analyzed by an effective admittivity tensor, and the responses of the current flow pathways and voltages were investigated at various applied current frequencies in the forward model. The linear reconstruction was performed following the sensitivity matrix approach at multiple frequencies. Simulation results achieved at various frequencies revealed that the anisotropy of the model was effectively reconstructed at low frequencies and disappeared at high frequencies, from which we validated the feasibility of multifrequency electrical impedance tomography method in reconstructing the anisotropic directions of the considered object.
View details for DOI 10.1002/cnm.2980
View details for Web of Science ID 000438341600001
View details for PubMedID 29521020
Electroencephalogram-Electromyography Coupling Analysis in Stroke Based on Symbolic Transfer Entropy.
Frontiers in neurology
2017; 8: 716
The coupling strength between electroencephalogram (EEG) and electromyography (EMG) signals during motion control reflects the interaction between the cerebral motor cortex and muscles. Therefore, neuromuscular coupling characterization is instructive in assessing motor function. In this study, to overcome the limitation of losing the characteristics of signals in conventional time series symbolization methods, a variable scale symbolic transfer entropy (VS-STE) analysis approach was proposed for corticomuscular coupling evaluation. Post-stroke patients (n=5) and healthy volunteers (n=7) were recruited and participated in various tasks (left and right hand gripping, elbow bending). The proposed VS-STE was employed to evaluate the corticomuscular coupling strength between the EEG signal measured from the motor cortex and EMG signal measured from the upper limb in both the time-domain and frequency-domain. Results showed a greater strength of the bi-directional (EEG-to-EMG and EMG-to-EEG) VS-STE in post-stroke patients compared to healthy controls. In addition, the strongest EEG-EMG coupling strength was observed in the beta frequency band (15-35Hz) during the upper limb movement. The predefined coupling strength of EMG-to-EEG in the affected side of the patient was larger than that of EEG-to-EMG. In conclusion, the results suggested that the corticomuscular coupling is bi-directional, and the proposed VS-STE can be used to quantitatively characterize the non-linear synchronization characteristics and information interaction between the primary motor cortex and muscles.
View details for DOI 10.3389/fneur.2017.00716
View details for PubMedID 29354091
Blood Oxygenation Changes Resulting From Subthreshold High Frequency Repetitive Transcranial Magnetic Stimulation
IEEE. 2017: 292–95
View details for Web of Science ID 000428143200070
A Simplified Hybrid EEG-fNIRS Brain-Computer Interface for Motor Task Classification
IEEE. 2017: 134–37
View details for Web of Science ID 000428143200032
Blood Oxygenation Changes Resulting From Subthreshold High Frequency Repetitive Transcranial Magnetic Stimulation
IEEE. 2017: 1513–16
Effects of high frequency repetitive transcranial magnetic stimulation (rTMS) with a subthreshold intensity on hemodynamic response in brain cortices (both motor and prefrontal cortices) was investigated using the functional near infrared spectroscopy (fNIRS) technique. FNIRS signals of the motor and prefrontal cortices were acquired in healthy volunteers (n=7) at rest and during rTMS intervention. A significant reduction in oxygenated hemoglobin (HbO) concentration during the entire stimulation process was observed from both motor and prefrontal cortices. Results showed that the fNIRS technique can provide a reliable measure of regional cortical brain activation that could be valuable in studying cortical excitability connectivity in combination with rTMS.
View details for Web of Science ID 000427085301239
View details for PubMedID 29060167
- The design and control of a 3DOF lower limb rehabilitation robot MECHATRONICS 2016; 33: 13–22
- Preliminary Study of Assessing Bladder Urinary Volume Using Electrical Impedance Tomography JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2016; 36 (1): 71–79
An Ankle Rehabilitation Device and Its Control Strategy
IEEE. 2014: 3267–72
View details for Web of Science ID 000393066203052