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< Page ,Total 31 >
Facial Expressions-Controlled Flight Game With Haptic Feedback for Stroke Rehabilitation: A Proof-of-Concept Study EI SCIE Scopus
期刊论文 | 2022 , 7 (3) , 6351-6358 | IEEE ROBOTICS AND AUTOMATION LETTERS
SCOPUS Cited Count: 1
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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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Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data EI SCIE Scopus
期刊论文 | 2022 , 71 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
SCOPUS Cited Count: 54
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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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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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Abstract :

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 :

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

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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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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 :

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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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 :

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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Attention Enhancement for Exoskeleton-Assisted Hand Rehabilitation Using Fingertip Haptic Stimulation. PubMed
期刊论文 | 2021 , 8 , 602091 | Frontiers in robotics and AI
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Abstract :

Active enrollment in rehabilitation training yields better treatment outcomes. This paper introduces an exoskeleton-assisted hand rehabilitation system. It is the first attempt to combine fingertip cutaneous haptic stimulation with exoskeleton-assisted hand rehabilitation for training participation enhancement. For the first time, soft material 3D printing techniques are adopted to make soft pneumatic fingertip haptic feedback actuators to achieve cheaper and faster iterations of prototype designs with consistent quality. The fingertip haptic stimulation is synchronized with the motion of our hand exoskeleton. The contact force of the fingertips resulted from a virtual interaction with a glass of water was based on data collected from normal hand motions to grasp a glass of water. System characterization experiments were conducted and exoskeleton-assisted hand motion with and without the fingertip cutaneous haptic stimulation were compared in an experiment involving healthy human subjects. Users' attention levels were monitored in the motion control process using a Brainlink EEG-recording device and software. The results of characterization experiments show that our created haptic actuators are lightweight (6.8 ± 0.23 g each with a PLA fixture and Velcro) and their performance is consistent and stable with small hysteresis. The user study experimental results show that participants had significantly higher attention levels with additional haptic stimulations compared to when only the exoskeleton was deployed; heavier stimulated grasping weight (a 300 g glass) was associated with significantly higher attention levels of the participants compared to when lighter stimulated grasping weight (a 150 g glass) was applied. We conclude that haptic stimulations increase the involvement level of human subjects during exoskeleton-assisted hand exercises. Potentially, the proposed exoskeleton-assisted hand rehabilitation with fingertip stimulation may better attract user's attention during treatment.

Keyword :

fingertip haptic stimulation hand exoskeleton hand rehabilitation haptic feedback pneumatic haptic actuator robot-assisted hand rehabilitation

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GB/T 7714 Li Min , Chen Jiazhou , He Guoying et al. Attention Enhancement for Exoskeleton-Assisted Hand Rehabilitation Using Fingertip Haptic Stimulation. [J]. | Frontiers in robotics and AI , 2021 , 8 : 602091 .
MLA Li Min et al. "Attention Enhancement for Exoskeleton-Assisted Hand Rehabilitation Using Fingertip Haptic Stimulation." . | Frontiers in robotics and AI 8 (2021) : 602091 .
APA Li Min , Chen Jiazhou , He Guoying , Cui Lei , Chen Chaoyang , Secco Emanuele Lindo et al. Attention Enhancement for Exoskeleton-Assisted Hand Rehabilitation Using Fingertip Haptic Stimulation. . | Frontiers in robotics and AI , 2021 , 8 , 602091 .
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Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance EI SCIE PubMed
期刊论文 | 2021 , 18 (5) | JOURNAL OF NEURAL ENGINEERING
WoS CC Cited Count: 2
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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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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: 6
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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&#8211 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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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 :

Brain computer interface Electroencephalography Electrophysiology Surface measurement Vision

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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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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 :

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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