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学者姓名：徐光华
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Abstract ：
Most stroke patients suffer from a combination of motor and sensory dysfunction and central facial paralysis. Specific rehabilitation training is required to restore those functions. Current research focuses on developing stimulating and straightforward rehabilitation training processes so that patients adhere to the training at home after hospital release. This study proposes enhancing patients' enthusiasm to participate in facial muscle exercises and improving their postural perception and balance by controlling virtual objects to complete assigned tasks in virtual reality games using different facial expressions with the assistance of haptic feedback. The different rehabilitation exercises for motor, sensory, and facial dysfunctions were combined in one virtual reality game for the first time. The proposed haptic feedback device was modeled, simulated, and characterized. A user study was conducted to validate the proposed system. The experiment result shows that all the designed functions of the comprehensive stroke rehabilitation virtual reality game can be achieved. The added haptic feedback enhances the performance of the aircraft control with facial expressions by lowering the trajectory deviation by 22.57%. This implies that the proposed game may improve users' performance, thus attracting them to conduct more training.
Keyword ：
Haptic feedback stroke rehabilitation virtual reality rehabilitation game
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| GB/T 7714 | Li, Min , Wu, Zonglin , Zhao, Chen-Guang et al. Facial Expressions-Controlled Flight Game With Haptic Feedback for Stroke Rehabilitation: A Proof-of-Concept Study [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2022 , 7 (3) : 6351-6358 . |
| MLA | Li, Min et al. "Facial Expressions-Controlled Flight Game With Haptic Feedback for Stroke Rehabilitation: A Proof-of-Concept Study" . | IEEE ROBOTICS AND AUTOMATION LETTERS 7 . 3 (2022) : 6351-6358 . |
| APA | Li, Min , Wu, Zonglin , Zhao, Chen-Guang , Yuan, Hua , Wang, Tianci , Xie, Jun et al. Facial Expressions-Controlled Flight Game With Haptic Feedback for Stroke Rehabilitation: A Proof-of-Concept Study . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2022 , 7 (3) , 6351-6358 . |
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Abstract ：
Recently, cross-domain fault diagnosis based on transfer learning methods has been extensively explored and well-addressed when class-balance data with supervision information are available. However, data under machine faulty states are mostly difficult to collect; there is a huge divide between current transfer learning methods based on implicit class-balance data and real industrial applications. In this article, we propose a class-imbalance adversarial transfer learning (CIATL) network with input being imbalanced data to learn domain-invariant and knowledge. Within this framework, class-imbalance learning is embedded into the adversarial training process to learn class-separate diagnostic knowledge with imbalanced data, double-level adversarial transfer learning including marginal and conditional distribution adaptations is conducted to learn domain-invariant knowledge. Extensive experiments on a planetary gearbox rig with imbalanced data verify the effectiveness and generalization of the proposed method and show its superior performance over contrastive transfer learning methods. Moreover, the proposed method relaxes the underlying assumption that the success of current transfer learning regimes is rooted in class-balance data and extends the application of the transfer learning method for real-industrial cross-domain diagnosis tasks.
Keyword ：
Adversarial training Classification algorithms class-imbalance learning Fault diagnosis Feature extraction Generative adversarial networks imbalanced data intelligent fault diagnosis Task analysis Training transfer learning Transfer learning
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| GB/T 7714 | Kuang, Jiachen , Xu, Guanghua , Tao, Tangfei et al. Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
| MLA | Kuang, Jiachen et al. "Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71 (2022) . |
| APA | Kuang, Jiachen , Xu, Guanghua , Tao, Tangfei , Wu, Qingqiang . Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2022 , 71 . |
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As a prime technique for proactive maintenance, bearing performance degradation assessment (PDA), which aims to build a health index (HI) to assess the performance degradation process, has drawn more and more attention in recent years. To construct an HI of high quality, we propose a novel and robust fuzzy c-means (FCM) model, based on locally linear embedding (LLE), that aims to learn a superficial correlated representation using a local mapping strategy. First, a great mass of commonly used features from the time-domain, the frequency-domain, and the time-frequency domain are extracted from the original vibration signature. Features are then implemented to obtain the initial dimensions by maximum likelihood estimation (MLE). Subsequently, local mapping produced by LLE with the initial dimensions extracts the underlying manifold structure from all the remaining features, and a superficial correlated representation is obtained, generated from the space expanded by the features. Finally, an HI based on the subjection of the FCM model is used to assess the bearing degradation process. To validate the superiority of the proposed method, it is compared with three advanced PDA models through experiments on three public datasets. A comparison of the proposed method with those of the other studies confirms the potential of MLE-LLE as an effective feature-fusion tool for the PDA of bearings. © 2021 IOP Publishing Ltd.
Keyword ：
Frequency domain analysis Mapping Maximum likelihood estimation Time domain analysis
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| GB/T 7714 | Kuang, Jiachen , Xu, Guanghua , Zhang, Sicong et al. Learning a superficial correlated representation using a local mapping strategy for bearing performance degradation assessment [J]. | Measurement Science and Technology , 2021 , 32 (6) . |
| MLA | Kuang, Jiachen et al. "Learning a superficial correlated representation using a local mapping strategy for bearing performance degradation assessment" . | Measurement Science and Technology 32 . 6 (2021) . |
| APA | Kuang, Jiachen , Xu, Guanghua , Zhang, Sicong , Wang, Bo . Learning a superficial correlated representation using a local mapping strategy for bearing performance degradation assessment . | Measurement Science and Technology , 2021 , 32 (6) . |
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In this paper, a novel method to diagnose eccentricity fault in rotor of three-phase induction motor based on the current signal, is proposed along with experimental validation. It utilizes three-phase current in time series into Clarks two phase alpha-beta 90 degrees apart vectors assimilating into 0 degrees measuring reference frame starting point with the aid of key-phasor averaging method. alpha-beta sinusoidal waves are applied to an interpolation method which gives an improved Fast Fourier transform of amplitude and phase angle of alpha-beta components. These transformed two-phase current signals are again measured to get amplitude and phase. Finally, the amplitude and phase of alpha-beta are used to generate holo-spectrum with an ellipse of the first order. Since unbalance fault occur at peak fundamental 1X harmonic frequency component, so its amplitude and phase angle are utilized for orbit construction. A comparison is made on rotor orbit and its eccentricity values, constructed from vibration signals and three-phase current signals, both measured simultaneously. This leads to a direct relationship in terms of orbit eccentricity and its longitudinal major axis inclination. The results show that at low frequency the eccentricity values of vibration and current signals orbits coincide hence the unbalance fault is detectable in induction motor by only utilizing the current signals. This method represents rotor non-linear dynamic behavior and is immune to unsymmetrical and anisotropic surface properties of the rotor and can measure rotor condition representing an accurate severity level of air gap eccentricity or, in mechanical terms, rotor unbalance fault. The new technique is a progressive development in induction motor air gap eccentricity fault detection and is applicable to harsh running conditions due to its simplified and non-invasive approach.
Keyword ：
air gap eccentricity holo spectrum severity level measurement three-phase induction motor
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| GB/T 7714 | Khalique, Umair , Xu, Guanghua , Zhang Xining et al. A novel detection method for diagnosis of rotor eccentricity in three-phase induction motor [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2021 , 32 (11) . |
| MLA | Khalique, Umair et al. "A novel detection method for diagnosis of rotor eccentricity in three-phase induction motor" . | MEASUREMENT SCIENCE AND TECHNOLOGY 32 . 11 (2021) . |
| APA | Khalique, Umair , Xu, Guanghua , Zhang Xining , Fei, Liu , Ahmad, Shahzad , Xun, Zhang et al. A novel detection method for diagnosis of rotor eccentricity in three-phase induction motor . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2021 , 32 (11) . |
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Abstract ：
Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain-computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio. Approach. Using the principle of nonlinear aperiodic FitzHugh-Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times. Results: A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise. Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.
Keyword ：
brain-computer interface FHN stochastic resonance single-channel TVEP signals the average-FHN method the FHN recovery method
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| GB/T 7714 | Chen, Ruiquan , Xu, Guanghua , Zheng, Yang et al. Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance [J]. | JOURNAL OF NEURAL ENGINEERING , 2021 , 18 (5) . |
| MLA | Chen, Ruiquan et al. "Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance" . | JOURNAL OF NEURAL ENGINEERING 18 . 5 (2021) . |
| APA | Chen, Ruiquan , Xu, Guanghua , Zheng, Yang , Yao, Pulin , Zhang, Sicong , Yan, Li et al. Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance . | JOURNAL OF NEURAL ENGINEERING , 2021 , 18 (5) . |
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Abstract ：
Objective. This study proposed and evaluated a channel ensemble approach to enhance detection of steady-state visual evoked potentials (SSVEPs). Approach. Collected multi-channel electroencephalogram signals were classified into multiple groups of new analysis signals based on correlation analysis, and each group of analysis signals contained signals from a different number of electrode channels. These groups of analysis signals were used as the input of a training-free feature extraction model, and the obtained feature coefficients were converted into feature probability values using the softmax function. The ensemble value of multiple sets of feature probability values was determined and used as the final discrimination coefficient. Main results. Compared with canonical correlation analysis, likelihood ratio test, and multivariate synchronization index analysis methods using a standard approach, the recognition accuracies of the methods using a channel ensemble approach were improved by 5.05%, 3.87%, and 3.42%, and the information transfer rates (ITRs) were improved by 6.00%, 4.61%, and 3.71%, respectively. The channel ensemble method also obtained better recognition results than the standard algorithm on the public dataset. This study validated the efficiency of the proposed method to enhance the detection of SSVEPs, demonstrating its potential use in practical brain-computer interface (BCI) systems. Significance. A SSVEP-based BCI system using a channel ensemble method could achieve high ITR, indicating great potential of this design for various applications with improved control and interaction.
Keyword ：
brain– channel ensemble computer interface motion stimulus steady-state visual evoked potential training-free algorithm
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| GB/T 7714 | Yan, Wenqiang , Du, Chenghang , Luo, Dan et al. Enhancing detection of steady-state visual evoked potentials using channel ensemble method [J]. | JOURNAL OF NEURAL ENGINEERING , 2021 , 18 (4) . |
| MLA | Yan, Wenqiang et al. "Enhancing detection of steady-state visual evoked potentials using channel ensemble method" . | JOURNAL OF NEURAL ENGINEERING 18 . 4 (2021) . |
| APA | Yan, Wenqiang , Du, Chenghang , Luo, Dan , Wu, YongCheng , Duan, Nan , Zheng, Xiaowei et al. Enhancing detection of steady-state visual evoked potentials using channel ensemble method . | JOURNAL OF NEURAL ENGINEERING , 2021 , 18 (4) . |
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Abstract ：
Due to the bypass of muscle activity or peripheral nerve control, brain-computer interface (BCI) technique has advantages in different fields such as medical field for the rehabilitation of paralyzed patients. Steady-state visual evoked potential (SSVEP) has been widely adopted in BCI applications. SSVEP based BCIs have the advantages of high information transfer rate, less or no need of training, and strong anti-interference, which could be used in the more natural asynchronous BCI application with control of the users rather than the operant system of the synchronous mode. In order to solve the problem of high false positive rate (FPR) in common asynchronous SSVEP BCI applications, this paper proposed an eye-tracking switch based BCI paradigm to reduce the FPR and to improve the performance of the asynchronous BCI system. In the proposed paradigm, the fixation point position instead of EEG signal is used to determine whether the system is in idle state. Experimental results showed that when eye-tracking switch was applied in the asynchronous SSVEP BCI, the FPR was reduced to less than 10% and the recognition accuracy (i.e., the true positive rate, TPR) can also be improved to a certain extent, which proved the applicability of the eye-tracking switch in asynchronous BCI applications. © 2021 IEEE.
Keyword ：
Brain Brain computer interface Eye tracking Interfaces (computer) Interface states Medical computing Patient rehabilitation
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| GB/T 7714 | Xie, Jun , Zhang, Huanqing , Liu, Yi et al. Asynchronous steady-state visual evoked potential brain-computer interface application: True and false positive rate comparison between with and without eye-tracking switch paradigms [C] . 2021 : 438-443 . |
| MLA | Xie, Jun et al. "Asynchronous steady-state visual evoked potential brain-computer interface application: True and false positive rate comparison between with and without eye-tracking switch paradigms" . (2021) : 438-443 . |
| APA | Xie, Jun , Zhang, Huanqing , Liu, Yi , Fang, Peng , Yu, Hongwei , He, Liushi et al. Asynchronous steady-state visual evoked potential brain-computer interface application: True and false positive rate comparison between with and without eye-tracking switch paradigms . (2021) : 438-443 . |
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Abstract ：
The non-stationary characteristics of surface electromyography (sEMG) and possible adverse variations in real-world conditions make it still an open challenge to realize robust myoelectric control (MEC) for multifunctional prostheses. Variable muscle contraction level is one of the handicaps that may degrade the performance of MEC. In this study, we proposed a force-invariant intent recognition method based on muscle synergy analysis (MSA) in the setting of three self-defined force levels (low, medium, and high). Specifically, a fast matrix factorization algorithm based on alternating non-negativity constrained least squares (NMF/ANLS) was chosen to extract task-specific synergies associated with each of six hand gestures in the training stage; while for the testing samples, we used the non-negative least square (NNLS) method to estimate neural commands for movement classification. The performance of proposed method was compared with conventional pattern recognition (PR) method consisting of LDA (linear discrimination analysis) classifier and representative features in three offline evaluation scenarios. Statistical tests on ten able-bodied subjects revealed no significant difference in intra-force-level (p = 0.353) and multi-force-level (p = 0.695) accuracy; But the synergy-based method performed significantly better than conventional PR-based method under inter-force-level conditions (p < 0.05). Similar results were observed for nine amputee subjects though there was a drop in the classification accuracy. This study was the first to concurrently demonstrate the robustness and predictive power of task-specific synergies under variant force levels and explore their potential for reliable intent recognition against force variation. Although the online performance is yet to be demonstrated, the proposed method is characterized by simple training procedure and acceptable computational efficiency, which would potentially provide an alternative approach for the development of clinically viable prostheses and rehabilitation robots driven by sEMG.
Keyword ：
Band-pass filters Electromyography Feature extraction Force multifunctional prostheses muscle contraction level Muscles muscle synergy myoelectric control sEMG Task analysis Wrist
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| GB/T 7714 | Teng, Zhicheng , Xu, Guanghua , Liang, Renghao et al. Evaluation of Synergy-Based Hand Gesture Recognition Method Against Force Variation for Robust Myoelectric Control [J]. | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2021 , 29 : 2345-2354 . |
| MLA | Teng, Zhicheng et al. "Evaluation of Synergy-Based Hand Gesture Recognition Method Against Force Variation for Robust Myoelectric Control" . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 29 (2021) : 2345-2354 . |
| APA | Teng, Zhicheng , Xu, Guanghua , Liang, Renghao , Li, Min , Zhang, Sicong . Evaluation of Synergy-Based Hand Gesture Recognition Method Against Force Variation for Robust Myoelectric Control . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2021 , 29 , 2345-2354 . |
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Abstract ：
The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland-Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (-0.095 logMAR), CCA (0.039 logMAR), and MSI (-0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.
Keyword ：
canonical correlation analysis multielectrode signals combination spatial filtering steady-state visual evoked potential visual acuity
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| GB/T 7714 | Zheng, Xiaowei , Xu, Guanghua , Han, Chengcheng et al. Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering [J]. | FRONTIERS IN NEUROSCIENCE , 2021 , 15 . |
| MLA | Zheng, Xiaowei et al. "Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering" . | FRONTIERS IN NEUROSCIENCE 15 (2021) . |
| APA | Zheng, Xiaowei , Xu, Guanghua , Han, Chengcheng , Tian, Peiyuan , Zhang, Kai , Liang, Renghao et al. Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering . | FRONTIERS IN NEUROSCIENCE , 2021 , 15 . |
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Abstract ：
Brain computer interface (BCI) is a novel communication method that does not rely on the normal neural pathway between the brain and muscle of human. It can transform mental activities into relevant commands to control external equipment and establish direct communication pathway. Among different paradigms, steady-state visual evoked potential (SSVEP) is widely used due to its certain periodicity and stability of control. However, electroencephalogram (EEG) of SSVEP is extremely weak and companied with multi-scale and strong noise. Existing algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of feature extraction under the multi-scale noise. Especially for the subjects produce weak response for external stimuli in EEG representation, i.e., BCI-Illiteracy subject, traditional algorithms are difficult to recognize the internal patterns of brain. To address this issue, a novel method based on Chaos theory is proposed to extract feature of SSVEP. The rule of this method is applying the peculiarity of nonlinear dynamics system to detect feature of SSVEP by judging the state changes of chaotic systems after adding weak EEG. To evaluate the validity of proposed method, this research recruit 32 subjects to participate the experiment. All subjects are divided into two groups according to the preliminary classification accuracy (mean acc >70% or < 70%) by canonical correlation analysis and we define the accuracy above 70% as group A (normal subjects), below 70% as group B (BCI-Illiteracy). Then, the classification accuracy and information transmission rate of two groups are verified using Chaotic theory. Experimental results show that all classification methods using in our study achieve good performance for normal subjects while chaos obtain excellent performance and significant improvements than traditional methods for BCI-Illiteracy.
Keyword ：
BCI-Illiteracy Brain computer interface (BCI) Brain modeling Chaos Chaotic detection (Chaos) electroencephalogram (EEG) Electroencephalography Feature extraction Force Mathematical model steady-state visual evoked potentials (SSVEP) Visualization
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| GB/T 7714 | Zhang, Kai , Xu, Guanghua , Du, Chenghang et al. Weak Feature Extraction and Strong Noise Suppression for SSVEP-EEG Based on Chaotic Detection Technology [J]. | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2021 , 29 : 862-871 . |
| MLA | Zhang, Kai et al. "Weak Feature Extraction and Strong Noise Suppression for SSVEP-EEG Based on Chaotic Detection Technology" . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 29 (2021) : 862-871 . |
| APA | Zhang, Kai , Xu, Guanghua , Du, Chenghang , Wu, Yongchen , Zheng, Xiaowei , Zhang, Sicong et al. Weak Feature Extraction and Strong Noise Suppression for SSVEP-EEG Based on Chaotic Detection Technology . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2021 , 29 , 862-871 . |
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