A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease.

Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.

Adaptive fuzzy control for tendon-sheath actuated bending-tip system with unknown friction for robotic flexible endoscope.

Introduction: The tendon-sheath actuated bending-tip system (TAB) has been widely applied to long-distance transmission scenes due to its high maneuverability, safety, and compliance, such as in exoskeleton robots, rescue robots, and surgical robots design. Due to the suitability of operation in a narrow or tortuous environment, TAB has demonstrated great application potential in the area of minimally invasive surgery. However, TAB involves highly non-linear behavior due to hysteresis, creepage, and non-linear friction existing on the tendon routing, which is an enormous challenge for accurate control. Methods: Considering the difficulties in the precise modeling of non-linearity friction, this paper proposes a novel fuzzy control scheme for the Euler-Lagrange dynamics model of TAB for achieving tracking performance and providing accurate friction compensation. Finally, the asymptotic stability of the closed-loop system is proved theoretically and the effectiveness of the controller is verified by numerical simulation carried out in MATLAB/Simulink. Results: The desired angle can be reached quickly within 3 s by adopting the proposed controller without overshoot or oscillation in Tracking Experiment, demonstrating the regulation performance of the proposed control scheme. The proposed method still achieves the desired trajectory rapidly and accurately without steady-state errors in Varying-friction Experiment. The angle errors generated by external disturbances are < 1 deg under the proposed controller, which returns to zero in 2 s in Anti-disturbance Experiment. In contrast, comparative controllers take more time to be steady and are accompanied by oscillating and residual errors in all experiments. Discussion: The proposed method is model-free control and has no strict requirement for the dynamics model and friction model. It is proved that advanced tracking performance and real-time response can be guaranteed under the presence of unknown bounded non-linear friction and time-varying non-linear dynamics.

Brain Temporal-Spectral Functional Variability Reveals Neural Improvements of DBS Treatment for Disorders of Consciousness.

Deep brain stimulation (DBS) is establishing itself as a promising treatment for disorders of consciousness (DOC). Measuring consciousness changes is crucial in the optimization of DBS therapy for DOC patients. However, conventional measures use subjective metrics that limit the investigations of treatment-induced neural improvements. The focus of this study is to analyze the regulatory effects of DBS and explain the regulatory mechanism at the brain functional level for DOC patients. Specifically, this paper proposed a dynamic brain temporal-spectral analysis method to quantify DBS-induced brain functional variations in DOC patients. Functional near-infrared spectroscopy (fNIRS) that promised to evaluate consciousness levels was used to monitor brain variations of DOC patients. Specifically, a fNIRS-based experimental procedure with auditory stimuli was developed, and the brain activities during the procedure from thirteen DOC patients before and after the DBS treatment were recorded. Then, dynamic brain functional networks were formulated with a sliding-window correlation analysis of phase lag index. Afterwards, with respect to the temporal variations of global and regional networks, the variability of global efficiency, local efficiency, and clustering coefficient were extracted. Further, dynamic networks were converted into spectral representations by graph Fourier transform, and graph energy and diversity were formulated to assess the spectral global and regional variability. The results showed that DOC patients under DBS treatment exhibited increased global and regional functional variability that was significantly associated with consciousness improvements. Moreover, the functional variability in the right brain regions had a stronger correlation with consciousness enhancements than that in the left brain regions. Therefore, the proposed method well signifies DBS-induced brain functional variations in DOC patients, and the functional variability may serve as promising biomarkers for consciousness evaluations in DOC patients.

Subject-Specific Modeling of EEG-fNIRS Neurovascular Coupling by Task-Related Tensor Decomposition.

Neurovascular coupling (NVC) connects neural activity with hemodynamics and plays a vital role in sustaining brain function. Combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is a promising way to explore the NVC. However, the high-order property of EEG data and variability of hemodynamic response function (HRF) across subjects have not been well considered in existing NVC studies. In this study, we proposed a novel framework to enhance the subject-specific parametric modeling of NVC from simultaneous EEG-fNIRS measurement. Specifically, task-related tensor decomposition of high-order EEG data was performed to extract the underlying connections in the temporal-spectral-spatial structures of EEG activities and identify the most relevant temporal signature within multiple trials. Subject-specific HRFs were estimated by parameters optimization of a double gamma function model. A canonical motor task experiment was designed to induce neural activity and validate the effectiveness of the proposed framework. The results indicated that the proposed framework significantly improves the reproducibility of EEG components and the correlation between the predicted hemodynamic activities and the real fNIRS signal. Moreover, the estimated parameters characterized the NVC differences in the task with two speeds. Therefore, the proposed framework provides a feasible solution for the quantitative assessment of the NVC function.

fNIRS-based graph frequency analysis to identify mild cognitive impairment in Parkinson's disease.

BACKGROUND: Early identification of mild cognitive impairment (MCI) is essential for its treatment and the prevention of dementia in Parkinson's disease (PD). Existing approaches are mostly based on neuropsychological assessments, while brain activation and connection have not been well considered. NEW METHOD: This paper presents a neuroimaging-based graph frequency analysis method and the generated features to quantify the brain functional neurodegeneration and distinguish between PD-MCI patients and healthy controls. The Stroop color-word experiment was conducted with 20 PD-MCI patients and 34 healthy controls, and the brain activation was recorded with functional near-infrared spectroscopy (fNIRS). Then, the functional brain network was constructed based on Pearson's correlation coefficient calculation between every two fNIRS channels. Next, the functional brain network was represented as a graph and decomposed in the graph frequency domain through the graph Fourier transform (GFT) to obtain the eigenvector matrix. Total variation and weighted zero crossings of eigenvectors were defined and integrated to quantify functional interaction between brain regions and the spatial variability of the brain network in specific graph frequency ranges, respectively. After that, the features were employed in training a support vector machine (SVM) classifier. RESULTS: The presented method achieved a classification accuracy of 0.833 and an F1 score of 0.877, significantly outperforming existing methods and features. COMPARISON WITH EXISTING METHODS: Our method provided improved classification performance in the identification of PD-MCI. CONCLUSION: The results suggest that the presented graph frequency analysis method well identify PD-MCI patients and the generated features promise functional brain biomarkers for PD-MCI diagnosis.

Effective DBS treatment improves neural information transmission of patients with disorders of consciousness: an fNIRS study.

Objective.Deep brain stimulation (DBS) is a potential treatment that promotes the recovery of patients with disorders of consciousness (DOC). This study quantified the changes in consciousness and the neuromodulation effect of DBS on patients with DOC.Approach.Eleven patients were recruited for this study which consists of three conditions: 'Pre' (two days before DBS surgery), 'Post-On' (one month after surgery with stimulation), and 'Post-Off' (one month after surgery without stimulation). Functional near-infrared spectroscopy (fNIRS) was recorded from the frontal lobe, parietal lobe, and occipital lobe of patients during the experiment of auditory stimuli paradigm, in parallel with the coma recovery scale-revised (CRS-R) assessment. The brain hemodynamic states were defined and state transition acceleration was taken to quantify the information transmission strength of the brain network. Linear regression analysis was conducted between the changes in regional and global indicators and the changes in the CRS-R index.Main results.Significant correlation was observed between the changes in the global transition acceleration indicator and the changes in the CRS-R index (slope = 55.910,p< 0.001,R2= 0.732). For the regional indicators, similar correlations were found between the changes in the frontal lobe and parietal lobe indicators and the changes in the CRS-R index (slope = 46.612,p< 0.01,R2= 0.694; slope = 47.491,p< 0.01,R2= 0.676).Significance.Our study suggests that fNIRS-based brain hemodynamics transition analysis can signify the neuromodulation effect of DBS treatment on patients with DOC, and the transition acceleration indicator is a promising brain functional marker for DOC.

Single-Channel sEMG-Based Estimation of Knee Joint Angle Using a Decomposition Algorithm With a State-Space Model.

Accurate human motion estimation is crucial for effective and safe human-robot interaction when using robotic devices for rehabilitation or performance enhancement. Although surface electromyography (sEMG) signals have been widely used to estimate human movements, conventional sEMG-based methods, which need sEMG signals measured from multiple relevant muscles, are usually subject to some limitations, including interference between sEMG sensors and wearable robots/environment, complicated calibration, as well as discomfort during long-term routine use. Few methods have been proposed to deal with these limitations by using single-channel sEMG (i.e., reducing the sEMG sensors as much as possible). The main challenge for developing single-channel sEMG-based estimation methods is that high estimation accuracy is difficult to be guaranteed. To address this problem, we proposed an sEMG-driven state-space model combined with an sEMG decomposition algorithm to improve the accuracy of knee joint movement estimation based on single-channel sEMG signals measured from gastrocnemius. The effectiveness of the method was evaluated via both single- and multi-speed walking experiments with seven and four healthy subjects, respectively. The results showed that the normal root-mean-squared error of the estimated knee joint angle using the method could be limited to 15%. Moreover, this method is robust with respect to variations in walking speeds. The estimation performance of this method was basically comparable to that of state-of-the-art studies using multi-channel sEMG.

A novel plantar pressure analysis method to signify gait dynamics in Parkinson's disease.

Plantar pressure can signify the gait performance of patients with Parkinson's disease (PD). This study proposed a plantar pressure analysis method with the dynamics feature of the sub-regions plantar pressure signals. Specifically, each side's plantar pressure signals were divided into five sub-regions. Moreover, a dynamics feature extractor (DFE) was designed to extract features of the sub-regions signals. The radial basis function neural network (RBFNN) was used to learn and store gait dynamics. And a classification mechanism based on the output error in RBFNN was proposed. The classification accuracy of the proposed method achieved 100.00% in PD diagnosis and 95.89% in severity assessment on the online dataset, and 96.00% in severity assessment on our dataset. The experimental results suggested that the proposed method had the capability to signify the gait dynamics of PD patients.

Polysulfide induced synthesis of a MoS2 self-supporting electrode with wide-layer-spacing for efficient electrocatalytic water splitting.

Efficient non-noble metal bifunctional electrocatalysts can increase the conversion rate of electric energy in the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). Herein, a ball & sheet MoS2/Ni3S2 composite with wide-layer-spacing and high 1T-rich MoS2 is assembled on nickel foam (NF) via a two-step solvothermal method with polymeric sulfur (S-r-DIB) as the sulfur source. The obtained material serves as both the cathode and the anode toward overall water splitting in an alkaline electrolyte. The results proved that the interpenetration of MoS2/Ni3S2-p with a ball and sheet structure increased the material active surface area and exposed more catalytic active sites, which contributed to the penetration of solution and the transfer of charge/hydrion. Meanwhile, two different semiconductors of MoS2 and Ni3S2 along with the presence of ample active sulfur edge sites and few-layer, wide-layer-spacing structures of MoS2 lead to an outstanding electrocatalytic activity. In particular, the electrodes of MoS2/Ni3S2-p only need a battery voltage of 1.55 V at 10 mA cm-2. The bifunctional electrocatalyst MoS2/Ni3S2-p also shows excellent stability at large current densities during the electrochemical test.

Video-Based Quantification of Gait Impairments in Parkinson's Disease Using Skeleton-Silhouette Fusion Convolution Network.

Gait impairments are among the most common hallmarks of Parkinson's disease (PD), usually appearing in the early stage and becoming a major cause of disability with disease progression. Accurate assessment of gait features is critical to personalized rehabilitation for patients with PD, yet difficult to be routinely carried out as clinical diagnosis using rating scales relies heavily on clinical experience. Moreover, the popular rating scales cannot ensure fine quantification of gait impairments for patients with mild symptoms. Developing quantitative assessment methods that can be used in natural and home-based environments is highly demanded. In this study, we address the challenges by developing an automated video-based Parkinsonian gait assessment method using a novel skeleton-silhouette fusion convolution network. In addition, seven network-derived supplementary features, including critical aspects of gait impairment (gait velocity, arm swing, etc.), are extracted to provide continuous measures enhancing low-resolution clinical rating scales. Evaluation experiments were conducted on a dataset collected with 54 patients with early PD and 26 healthy controls. The results show that the proposed method accurately predicted the patients' unified Parkinson's disease rating scale (UPDRS) gait scores (71.25% match on clinical assessment) and discriminated between PD patients and healthy subjects with a sensitivity of 92.6%. Moreover, three proposed supplementary features (i.e., arm swing amplitude, gait velocity, and neck forward bending angle) turned out to be effective gait dysfunction indicators with Spearman correlation coefficients of 0.78, 0.73, and 0.43 matching the rating scores, respectively. Since the proposed system requires only two smartphones, it holds a significant benefit for home-based quantitative assessment of PD, especially for detecting early-stage PD. Furthermore, the proposed supplementary features can enable high-resolution assessments of PD for providing subject-specific accurate treatments.

[A two-dimensional video based quantification method and clinical application research of motion disorders].

The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson's disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.

An EEG-fNIRS neurovascular coupling analysis method to investigate cognitive-motor interference.

BACKGROUND AND OBJECTIVE: The simultaneous execution of a motor and cognitive dual task may lead to the deterioration of task performance in one or both tasks due to cognitive-motor interference (CMI). Neuroimaging techniques are promising ways to reveal the underlying neural mechanism of CMI. However, existing studies have only explored CMI from a single neuroimaging modality, which lack built-in validation and comparison of analysis results. This work is aimed to establish an effective analysis framework to comprehensively investigate the CMI by exploring the electrophysiological and hemodynamic activities as well as their neurovascular coupling. METHODS: Experiments including an upper limb single motor task, single cognitive task, and cognitive-motor dual task were designed and performed with 16 healthy young participants. Bimodal signals of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded simultaneously during the experiments. A novel bimodal signal analysis framework was proposed to extract the task-related components for EEG and fNIRS signals respectively and analyze their correlation. Indicators including within-class similarity and between-class distance were utilized to validate the effectiveness of the proposed analysis framework compared to the canonical channel-averaged method. Statistical analysis was performed to investigate the difference in the behavior and neural correlates between the single and dual tasks. RESULTS: Our results revealed that the extra cognitive interference caused divided attention in the dual task, which led to the decreased neurovascular coupling between fNIRS and EEG in all theta, alpha, and beta rhythms. The proposed framework was demonstrated to have a better ability in characterizing the neural patterns than the canonical channel-averaged method with significantly higher within-class similarity and between-class distance indicators. CONCLUSIONS: This study proposed a method to investigate CMI by exploring the task-related electrophysiological and hemodynamic activities as well as their neurovascular coupling. Our concurrent EEG-fNIRS study provides new insight into the EEG-fNIRS correlation analysis and novel evidence for the mechanism of neurovascular coupling in the CMI.

An fNIRS-Based Dynamic Functional Connectivity Analysis Method to Signify Functional Neurodegeneration of Parkinson's Disease.

Parkinson's disease (PD) is a prevalent brain disorder, and PD diagnosis is crucial for treatment. Existing methods for PD diagnosis are mainly focused on behavior analysis, while the functional neurodegeneration of PD has not been well investigated. This paper proposes a method to signify functional neurodegeneration of PD with dynamic functional connectivity analysis. A functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation from 50 PD patients and 41 age-matched healthy controls in clinical walking tests. Dynamic functional connectivity was constructed with sliding-window correlation analysis, and k-means clustering was applied to generate the key brain connectivity states. Dynamic state features including state occurrence probability, state transition percentage and state statistical features were extracted to quantify the variations of brain functional networks. A support vector machine was trained to classify PD patients and healthy controls. Statistical analysis was conducted to investigate the difference between PD patients and healthy controls as well as the relationship between dynamic state features and the MDS-UPDRS sub-score of gait. The results showed that PD patients had a higher probability of transiting to brain connectivity states with high levels of information transmission compared with healthy controls. The MDS-UPDRS sub-score of gait and the dynamics state features showed a significant correlation. Moreover, the proposed method had better classification performances than the available fNIRS-based methods in terms of accuracy and F1 score. Thus, the proposed method well signified functional neurodegeneration of PD, and the dynamic state features may serve as promising functional biomarkers for PD diagnosis.

Increased Effective Connectivity of the Left Parietal Lobe During Walking Tasks in Parkinson's Disease.

BACKGROUND: In Parkinson's disease (PD), walking may depend on the activation of the cerebral cortex. Understanding the patterns of interaction between cortical regions during walking tasks is of great importance. OBJECTIVE: This study investigated differences in the effective connectivity (EC) of the cerebral cortex during walking tasks in individuals with PD and healthy controls. METHODS: We evaluated 30 individuals with PD (62.4±7.2 years) and 22 age-matched healthy controls (61.0±6.4 years). A mobile functional near-infrared spectroscopy (fNIRS) was used to record cerebral oxygenation signals in the left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left parietal lobe (LPL), and right parietal lobe (RPL) and analyze the EC of the cerebral cortex. A wireless movement monitor was used to measure the gait parameters. RESULTS: Individuals with PD demonstrated a primary coupling direction from LPL to LPFC during walking tasks, whereas healthy controls did not demonstrate any main coupling direction. Compared with healthy controls, individuals with PD showed statistically significantly increased EC coupling strength from LPL to LPFC, from LPL to RPFC, and from LPL to RPL. Individuals with PD showed decreased gait speed and stride length and increased variability in speed and stride length. The EC coupling strength from LPL to RPFC negatively correlated with speed and positively correlated with speed variability in individuals with PD. CONCLUSION: In individuals with PD, the left prefrontal cortex may be regulated by the left parietal lobe during walking. This may be the result of functional compensation in the left parietal lobe.

fNIRS-based functional connectivity signifies recovery in patients with disorders of consciousness after DBS treatment.

OBJECTIVE: While deep brain stimulation (DBS) has proved effective for certain patients with disorders of consciousness (DOC), the working neural mechanism is not clear, the response varies for patients, and the assessment is inadequate. This paper aims to quantify the DBS-induced changes of consciousness in DOC patients at the neural functional level. METHODS: Ten DOC patients were included for DBS surgery. The DBS target was the right centromedian-parafascicular (CM-pf) nuclei for four patients and the bilateral CM-pf nuclei for six patients. Functional near-infrared spectroscopy (fNIRS) was taken to measure the neural activation of patients, in parallel with Coma Recovery Scale-Revised (CRS-R), before the DBS surgery and one month after. The fNIRS signals were recorded from the frontal, parietal, and occipital lobes. Functional connectivity analysis quantified the communication between brain regions, area communication strength, and global communication efficiency. Linear regression analysis was conducted between the changes of indices based on functional connectivity analysis and the changes of the CRS-R index. RESULTS: Patients with trauma (n = 4) exhibited a greater increase of CRS-R scores after DBS treatment compared with patients with hemorrhage (n = 4) and brainstem infarction (n = 2). Global communication efficiency changed consistently with the CRS-R index (slope = 57.384, p < 0.05, R2=0.483). No significant relationship was found between the changes of area communication strength of six brain lobes and the changes of the CRS-R index. CONCLUSIONS: The cause of DOC is essential for the outcome of DBS treatment, and brain communication efficiency is a promising functional marker for DOC recovery. SIGNIFICANCE: fNIRS-based functional connectivity analysis on brain network signifies changes of consciousness in DOC patients after DBS treatment.

[A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image segmentation].

Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the target domain. This paper proposed an unsupervised end-to-end domain adaptation medical image segmentation method based on the generative adversarial network (GAN). A network training and adjustment model was designed, including segmentation and discriminant networks. In the segmentation network, the residual module was used as the basic module to increase feature reusability and reduce model optimization difficulty. Further, it learned cross-domain features at the image feature level with the help of the discriminant network and a combination of segmentation loss with adversarial loss. The discriminant network took the convolutional neural network and used the labels from the source domain, to distinguish whether the segmentation result of the generated network is from the source domain or the target domain. The whole training process was unsupervised. The proposed method was tested with experiments on a public dataset of knee magnetic resonance (MR) images and the clinical dataset from our cooperative hospital. With our method, the mean Dice similarity coefficient (DSC) of segmentation results increased by 2.52% and 6.10% to the classical feature level and image level domain adaptive method. The proposed method effectively improves the domain adaptive ability of the segmentation method, significantly improves the segmentation accuracy of the tibia and femur, and can better solve the domain transfer problem in MR image segmentation.

Fronto-parietal cortex activation during walking in patients with Parkinson's disease adopting different postural strategies.

Background: Cortical activation patterns in patients with Parkinson's disease (PD) may be influenced by postural strategies, but the underlying neural mechanisms remain unclear. Our aim is to examine the role of the fronto-parietal lobes in patients with PD adopting different postural strategies and the effect of dual task (DT) on fronto-parietal activation. Methods: Two groups of patients with PD adopting either the posture first strategy (PD-PF) or the posture second strategy (PD-PS) were examined respectively when in the "OFF" state while single-walking task (SW) and DT. Frontal and parietal lobe activity was assessed by functional near infrared spectroscopy (fNIRS) and measuring gait parameters. Linear mixed models were used for analyses. Results: Patients with PD who adopted PS had greater cortical activation than those who adopted PF, and there was no difference between PF and PS in the behavioral parameters. For oxyhemoglobin levels, the task condition (SW vs. DT) had a main effect in fronto-parietal lobes. Postural strategy (PD-PF vs. PD-PS) a main effect in the left prefrontal cortex (LPFC), left parietal lobe (LPL), and right parietal lobe (RPL) regions. In the task of walking with and without the cognitive task, patients with PD adopting PS had higher activation in the LPL than those adopting PF. In DT, only PD patients who adopted PS had elevated oxyhemoglobin levels in the LPFC, right prefrontal cortex (RPFC), and LPL compared with the SW, whereas patients with PD who adopted PF showed no differences in any region. Conclusion: Different patterns of fronto-parietal activation exist between PD-PF and PD-PS. This may be because PD-PS require greater cortical functional compensation than those adopting PF.

Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network.

Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimating human's intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use. Although the muscle synergy principle implies that it is possible to estimate human motion using sEMG signals from even one signal muscle, few studies investigated the feasibility of continuous motion estimation based on single-channel sEMG. In this study, a feature-guided convolutional neural network (FG-CNN) has been proposed to estimate human knee joint movement using single-channel sEMG. In the proposed FG-CNN, several handcrafted features have been fused into a CNN model to guide CNN feature extraction, and both handcrafted and CNN-extracted features were applied to a regression model, i.e., random forest regression, to estimate knee joint movements. Experiments with 8 healthy subjects were carried out, and sEMG signals measured from 6 muscles, i.e., vastus lateralis, vastus medialis, biceps femoris, semitendinosus, lateral or medial gastrocnemius (LG or MG), were separately evaluated for knee joint estimation using the proposed method. The experimental results demonstrated that the proposed FG-CNN method with single-channel sEMG signals from LG or MG can effectively estimate human knee joint movements. The average correlation coefficient between the measured and the estimated knee joint movements is 0.858 ± 0.085 for LG and 0.856 ± 0.057 for MG. Meanwhile, comparative studies showed that the combined handcrafted-CNN features outperform either the handcrafted features or the CNN features; the performance of the proposed signal-channel sEMG-based FG-CNN method is comparable to those of the traditional multi-channel sEMG-based methods. The outcomes of this study enable the possibility of developing a single-channel sEMG-based human-robot interface for knee joint movement estimation, which can facilitate the routine use of assistive robots.

A Functional Region Decomposition Method to Enhance fNIRS Classification of Mental States.

Functional near-infrared spectroscopy (fNIRS) classification of mental states is of important significance in many neuroscience and clinical applications. Existing classification algorithms use all signal-collected brain regions as a whole, and brain sub-region contributions have not been well investigated. This paper proposes a functional region decomposition (FRD) method to incorporate brain sub-region contributions and enhance fNIRS classification of mental states. Specifically, the method iteratively decomposes the brain region into multiple sub-regions to maximize their contributions with respect to the validation accuracy and coverage of brain sub-regions. Then for the fNIRS data in brain sub-regions, features are extracted and classified to output the predictions. The final predictions are determined by fusing predictions from multiple brain sub-regions with stacking. Experiments on a publicly available fNIRS dataset showed that the proposed functional region decomposition method led to 9.01% and 10.58% increase of classification accuracy for the methods related to slope-based features and mean concentration change features, respectively. Therefore, the proposed method can decompose the brain region into sub-regions with respect to their functional contributions and fundamentally enhance the performance of mental state classification.

fNIRS-based brain state transition features to signify functional degeneration after Parkinson's disease.

Objective.Parkinson's disease (PD) is a common neurodegenerative brain disorder, and early diagnosis is of vital importance for treatment. Existing methods are mainly focused on behavior examination, while the functional neurodegeneration after PD has not been well explored. This paper aims to investigate the brain functional variation of PD patients in comparison with healthy controls.Approach.In this work, we propose brain hemodynamic states and state transition features to signify functional degeneration after PD. Firstly, a functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation during dual-task walking from PD patients and healthy controls. Then, three brain states, named expansion, contraction, and intermediate states, were defined with respect to the oxyhemoglobin and deoxyhemoglobin responses. After that, two features were designed from a constructed transition factor and concurrent variations of oxy- and deoxy-hemoglobin over time, to quantify the transitions of brain states. Further, a support vector machine classifier was trained with the proposed features to distinguish PD patients and healthy controls.Main results.Experimental results showed that our method with the proposed brain state transition features achieved classification accuracy of 0.8200 andFscore of 0.9091, and outperformed existing fNIRS-based methods. Compared with healthy controls, PD patients had significantly smaller transition acceleration and transition angle.Significance.The proposed brain state transition features well signify functional degeneration of PD patients and may serve as promising functional biomarkers for PD diagnosis.