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< Page ,Total 31 >
A novel detection method for diagnosis of rotor eccentricity in three-phase induction motor EI SCIE
期刊论文 | 2021 , 32 (11) | MEASUREMENT SCIENCE AND TECHNOLOGY
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Abstract :

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 :

severity level measurement three-phase induction motor air gap eccentricity holo spectrum

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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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Measurement and evaluation of cylindricity deviation in Cartesian coordinates EI SCIE Scopus
期刊论文 | 2021 , 32 (3) | MEASUREMENT SCIENCE AND TECHNOLOGY
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Abstract :

In the earlier studies of the Liu et al (2015 Intersecting chord method for minimum zone evaluation of roundness deviation using Cartesian coordinate data Precis. Eng. 42 242-52, 2016 Minimum circumscribed circle and maximum inscribed circle of roundness deviation evaluation with intersecting chord method IEEE Trans. Instrum. Meas. 65 2787-96), the intersecting chords method was proposed for modeling roundness and sphericity deviations. Because cylindricity deviation is partially associated with both deviations, this study focuses on the measurement and evaluation of cylindricity deviations using the intersecting chords method in the Cartesian coordinate system. Combined with the geometric structure of a cylinder, the measurement method of the cylindricity deviation in Cartesian coordinates is introduced, and the cylindricity measurement is conducted by multi-section sampling, axis fitting, and spatial projection. Based on this, the intersecting chords method is employed to construct models for the cylindricity deviation evaluation using the point coordinates obtained from the cylindricity measurements, including minimum circumscribed cylindricity, maximum inscribed cylindricity, and minimum zone cylindricity. The core of the intersecting chords method is using a chord instead of a curve as the characteristic element of the evaluation model. Moreover, the cross structure produced by the intersecting chords can control the position of the reference form and obtain the center of the evaluation model accurately. For the cylindricity deviation, the intersecting chords method not only reduces the modeling difficulty but also improves the evaluation accuracy. Moreover, the availability and validity of the modeling method are further verified by the experimental data obtained from coordinate measuring machines, and the results show that the research study is useful for developing measurement instruments and manufacturing equipment having large cylindrical parts.

Keyword :

intersecting chords form deviation evaluation Cartesian coordinates cylindricity deviation

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GB/T 7714 Liu, Fei , Liang, Lin , Xu, Guanghua et al. Measurement and evaluation of cylindricity deviation in Cartesian coordinates [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2021 , 32 (3) .
MLA Liu, Fei et al. "Measurement and evaluation of cylindricity deviation in Cartesian coordinates" . | MEASUREMENT SCIENCE AND TECHNOLOGY 32 . 3 (2021) .
APA Liu, Fei , Liang, Lin , Xu, Guanghua , Hou, Chenggang , Liu, Dan . Measurement and evaluation of cylindricity deviation in Cartesian coordinates . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2021 , 32 (3) .
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Learning a superficial correlated representation using a local mapping strategy for bearing performance degradation assessment EI SCIE
期刊论文 | 2021 , 32 (6) | Measurement Science and Technology
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Abstract :

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 :

Mapping Maximum likelihood estimation Time domain analysis Frequency 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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Three-Dimensional Pose Estimation of Infants Lying Supine Using Data from a Kinect Sensor with Low Training Cost EI SCIE
期刊论文 | 2021 , 21 (5) , 6904-6913 | IEEE Sensors Journal
WoS CC Cited Count: 1
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Early diagnosis of cerebral palsy in infants has produced promising results using tools like the General Movement Assessment (GMA). Pose estimation of infants lying supine is an important step towards an automated system for GMA. Developing methods for accurate, reliable, fast estimation of the three-dimensional (3D) position of human limbs and proposing motion features suitable for 3D models to classify typical and atypical movements are attracting increasing research interest lately. In this study, we propose a 3D pose estimation method with low training cost suitable for infants in lying positions. The method uses an existing two-dimensional human body keypoint detection method combined with the in-depth information in Red-Green-Blue Depth (RGB-D) data from a Kinect sensor. The method is evaluated using the Moving INfants In RGB-D (MINI-RGBD) open dataset. The results show that the average error of the estimated body part length is 13.76 mm, while the accuracy of the Percentage of Correctly-localized Parts (PCP) and Percentage of Correct Keypoint (PCK) is 80.7 and 86.1%, respectively. The results are comparable to those achieved in the baseline study performed by the researchers who generated this open dataset. The advantage of our method is its low training cost. © 2001-2012 IEEE.

Keyword :

Cost estimating Diagnosis Automation Gas metal arc welding

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GB/T 7714 Li, Min , Wei, Fan , Li, Yu et al. Three-Dimensional Pose Estimation of Infants Lying Supine Using Data from a Kinect Sensor with Low Training Cost [J]. | IEEE Sensors Journal , 2021 , 21 (5) : 6904-6913 .
MLA Li, Min et al. "Three-Dimensional Pose Estimation of Infants Lying Supine Using Data from a Kinect Sensor with Low Training Cost" . | IEEE Sensors Journal 21 . 5 (2021) : 6904-6913 .
APA Li, Min , Wei, Fan , Li, Yu , Zhang, Sicong , Xu, Guanghua . Three-Dimensional Pose Estimation of Infants Lying Supine Using Data from a Kinect Sensor with Low Training Cost . | IEEE Sensors Journal , 2021 , 21 (5) , 6904-6913 .
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Research Progress in Objective Visual Acuity Examination Technology Based on Visual Evoked Potential EI
期刊论文 | 2021 , 55 (3) , 1-10 | Journal of Xi'an Jiaotong University
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Visual evoked potential (VEP) is electrical signal induced by visual stimulation and generated in the visual cortex of the brain, which can be collected and analyzed by electrodes on the scalp. VEP provides an objective and quantitative method for visual acuity estimation, especially in non-verbal infants, adults with low intellectual abilities or malingering, and patients with various visual impairments. Aiming at the VEP visual acuity examination technology, this paper reviewed and compared the relevant literature from the aspects of electroencephalogram (EEG) equipment platform, visual stimulation paradigm, experimental parameter setting, visual acuity threshold determination algorithm and clinical application. It is found that in the EEG equipment platform, the current researches almost depend on the dedicated brain-computer interface (BCI) systems, lacking commercial equipment. In terms of stimulation paradigm, compared with the traditional grating and checkerboard patterns, the concentric-ring stimulation paradigm of oscillating contraction and expansion shows its excellent anti-fatigue performance and low contrast sensitivity. According to the experimental parameter setting, the temporal frequencies of 7.5 Hz and 1 Hz of steady-state and transient VEP, medium-value of luminance and contrast, and the spatial frequency range of 3-30 cycles/(°) are mostly adopted. In threshold determination algorithm, the most commonly used strategies are linear extrapolation method and minimum size method. In clinical application, VEP technology is mainly used in the assessment of children's visual acuity development and the objective visual acuity evaluation in various visual diseases. This review shows that VEP visual acuity assessment technology urgently needs a unified standard, so that it can be expected to have wider and faster applications and excites the research on VEP and even electrophysiology in vision function detection and diagnosis at higher level. © 2021, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.

Keyword :

Electrophysiology Electroencephalography Surface measurement Vision Brain computer interface

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GB/T 7714 Xu, Guanghua , Zheng, Xiaowei , Tian, Peiyuan et al. Research Progress in Objective Visual Acuity Examination Technology Based on Visual Evoked Potential [J]. | Journal of Xi'an Jiaotong University , 2021 , 55 (3) : 1-10 .
MLA Xu, Guanghua et al. "Research Progress in Objective Visual Acuity Examination Technology Based on Visual Evoked Potential" . | Journal of Xi'an Jiaotong University 55 . 3 (2021) : 1-10 .
APA Xu, Guanghua , Zheng, Xiaowei , Tian, Peiyuan , Du, Chenghang , Zhang, Sicong . Research Progress in Objective Visual Acuity Examination Technology Based on Visual Evoked Potential . | Journal of Xi'an Jiaotong University , 2021 , 55 (3) , 1-10 .
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A Convolution Neural Network with Mixed-Size Kernels for Time-Frequency Characteristics of Motor Imagery EI
会议论文 | 2021 , 69-75 | 7th International Conference on Computing and Artificial Intelligence, ICCAI 2021
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As a paradigm of spontaneous Brain-Computer Interface (BCI), motor imagery electroencephalogram (EEG) has always been a hot topic in BCI and clinical rehabilitation. Many algorithms have been proposed for decoding the motor imagery signals. The algorithm based on the convolutional neural network has shown excellent potential in the task of motor imagery signal classification. However, the existing models are not specific to the characteristics of motor imagery signals, and so, they cannot fully extract the signal features of different rhythms. Moreover, limited by difficulties in the acquisition, the classification effect of the model is network based on mixed-size convolution kernel is designed. The time-frequency graph obtained by Short-time Fourier transform (STFT) is used as input, and Deep Convolutional Generative Adversarial Networks (DCGANs) is used for data enhancement. The results show that the average classification accuracy is 85.7%. Compared with current mainstream classification algorithms, the model presented in this paper has shown high classification accuracy and good robustness. © 2021 ACM.

Keyword :

Generative adversarial networks Convolution Biomedical signal processing Convolutional neural networks Brain computer interface Electroencephalography

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GB/T 7714 Tao, Tangfei , Han, Zezhen , Xu, Guanghua et al. A Convolution Neural Network with Mixed-Size Kernels for Time-Frequency Characteristics of Motor Imagery [C] . 2021 : 69-75 .
MLA Tao, Tangfei et al. "A Convolution Neural Network with Mixed-Size Kernels for Time-Frequency Characteristics of Motor Imagery" . (2021) : 69-75 .
APA Tao, Tangfei , Han, Zezhen , Xu, Guanghua , Zhang, Kai . A Convolution Neural Network with Mixed-Size Kernels for Time-Frequency Characteristics of Motor Imagery . (2021) : 69-75 .
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Asynchronous steady-state visual evoked potential brain-computer interface application: True and false positive rate comparison between with and without eye-tracking switch paradigms EI
会议论文 | 2021 , 438-443 | 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021
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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 :

Patient rehabilitation Interface states Interfaces (computer) Brain computer interface Brain Eye tracking Medical computing

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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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Weak Feature Extraction and Strong Noise Suppression for SSVEP-EEG Based on Chaotic Detection Technology EI SCIE
期刊论文 | 2021 , 29 , 862-871 | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
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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 :

electroencephalogram (EEG) Brain computer interface (BCI) Brain modeling steady-state visual evoked potentials (SSVEP) Mathematical model Chaos Chaotic detection (Chaos) Visualization Electroencephalography Feature extraction Force BCI-Illiteracy

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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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Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition EI SCIE
期刊论文 | 2021 , 68 | Biomedical Signal Processing and Control
WoS CC Cited Count: 1
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Machine learning (ML) methods have been previously applied and compared in pattern recognition of hand and elbow motions based on surface electromyographic (sEMG) signals. However, there are only a few studies that have investigated the ML methods for shoulder motion pattern recognition. This study compared the efficiency of ML algorithms, including support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) in processing sEMG signals for shoulder motion pattern recognition. This study also investigated the the effects of sliding time window epoch on the recognition accuracy. Eighteen healthy subjects were recruited for this study, their EMG signals were collected from twelve muscles during performing activities of daily living (ADL) motions including drinking, pushing forward/pulling backward, and abduction/adduction. The 80 % of recoded sEMG datasets were used for model training to build the ML models and 20 % were used for model validation and determination of the accuracy of ML algorithms in motion pattern recognition. The influence of sliding time window sizes was studied for algorithm optimization. Statistical analysis was performed to determine the difference in the accuracy of ML methods. Results showed that there was a significant difference among the three machine learning methods and different sliding time window sizes. There was not a significant difference in overlapping time. The highest accuracy was 97.41 ± 1.8 % using the SVM method with a sliding time window of 270 ms. Machine learning techniques provided a quick approach for shoulder motion pattern recognition. The better classifier for pattern recognition of shoulder motion was SVM. © 2021 Elsevier Ltd

Keyword :

Support vector machines Pattern recognition Biomedical signal processing Neural networks Regression analysis Motion estimation

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GB/T 7714 Zhou, Yang , Chen, Chaoyang , Cheng, Mark et al. Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition [J]. | Biomedical Signal Processing and Control , 2021 , 68 .
MLA Zhou, Yang et al. "Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition" . | Biomedical Signal Processing and Control 68 (2021) .
APA Zhou, Yang , Chen, Chaoyang , Cheng, Mark , Alshahrani, Yousef , Franovic, Sreten , Lau, Emily et al. Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition . | Biomedical Signal Processing and Control , 2021 , 68 .
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Enhancing detection of steady-state visual evoked potentials using channel ensemble method EI SCIE PubMed
期刊论文 | 2021 , 18 (4) | JOURNAL OF NEURAL ENGINEERING
WoS CC Cited Count: 3
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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 :

training-free algorithm steady-state visual evoked potential channel ensemble computer interface brain&#8211 motion stimulus

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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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