Common spatial pattern(CSP) has been one of the most popular methods for EEG feature extraction in brain-computer interface(BCI) application. Although the CSP usually provides good discriminant features for classifica...
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Common spatial pattern(CSP) has been one of the most popular methods for EEG feature extraction in brain-computer interface(BCI) application. Although the CSP usually provides good discriminant features for classification,it is also known to be sensitive to overfitting and *** study introduces a shrinkage technique to regularize estimation of the covariance matrices in the CSP and hence a novel shrinkage CSP(SCSP) method,which could effectively alleviate the effects of small training sample size and unbalanced data on classification. The proposed SCSP is validated on feature extraction of P300 that has been widely adopted for BCI *** accuracies are evaluated by using linear discriminant analysis(LDA) with experimental EEG data from seven *** results indicate that the proposed SCSP extracts more effective features that yield higher classification accuracy than that by the traditional CSP.
Recently, a new system called brain control system has been developed rapidly. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human's i...
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Recently, a new system called brain control system has been developed rapidly. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human's ideas and thinking. Brain control system has been successfully applied in wide fields, assisting disabled patients daily life, training patients with stroke or limb injury, monitoring the state of human operator, as well as entertainment and smart house etc. In this paper, the background, basic principle, system structure and developments are firstly introduced briefly. The current research status focusing on the problems of electroencephalograph (EEG) signal pattern, control signal transfer algorithm and system application is summarized and analyzed in detail. The further research direction and problems are discussed. Finally, the future development of brain control is analyzed and prospects are given.
In this paper, the containment control problem is studied for the cooperative output regulation of linear multi-agent systems(MASs). The output regulation problem initiated from single-leader-follower region is extend...
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In this paper, the containment control problem is studied for the cooperative output regulation of linear multi-agent systems(MASs). The output regulation problem initiated from single-leader-follower region is extended to the multi-leadersfollower region. The purpose of this paper is to design a containment control algorithm such that all the followers can move into the convex hull formed by the leaders. A sufficient and necessary condition in term of a linear matrix inequality (LMI) is derived for the existence of a containment controller protocol that guarantees the followers are capable to follow the trail of leader and the output error will converge to zero with the time elapsing. Then, a controller design procedure is given to construct the feedback gain matrix and select the proper gain matrix for achieving final consensus of the MASs. Compared with the existing algorithms, the proposed one requires less information and calculation of the velocities measurement of the agents. Finally, a numerical example is illustrated to show the usefulness of the designed containment controller.
Brain-Computer Interface (BCI) is a novel communication system without depending on conventional brain output paths (such as peripheral nerve and muscle tissue) of the brain. The evaluation of effective EEG patterns i...
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Brain-Computer Interface (BCI) is a novel communication system without depending on conventional brain output paths (such as peripheral nerve and muscle tissue) of the brain. The evaluation of effective EEG patterns is one of the crucial issues in the current research of BCI. Most of the traditional visual evoked paradigms only evoke one kind of EEG pattern for the subsequent feature classification. This study presents a new paradigm based on P300 and Steady-State Visual Evoked Potential (SSVEP) that involves event-related stimulation and frequency flashing stimulation. P300 and SSVEP patterns are evoked simultaneously to enhance the discriminability of features. Offline comparison is implemented among the proposed paradigm and the traditional P300 and SSVEP paradigms. The results show that the new paradigm evokes more significant P300 features while weaken SSVEP features a little without destroying the online feasibility of the BCI system. Therefore, the proposed paradigm can satisfy requirements from different subjects to enlarge the user of group.
This paper proposes an artificial neural network (ANN) based time/space separation modeling approach to predict nonlinear parabolic DPSs. First, the spatial-temporal output is divided into a few dominant spatial basis...
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This paper proposes an artificial neural network (ANN) based time/space separation modeling approach to predict nonlinear parabolic DPSs. First, the spatial-temporal output is divided into a few dominant spatial basis functions and low-dimensional time series by PCA method. Then a three-layer feed-forward ANN is identified by low-dimensional time series, where the improved group search optimization (GSO) is proposed to optimize the connection weights and thresholds to solve the problem of falling into the local optima. Finally, the nonlinear spatiotemporal dynamics is determined after the time/space reconstruction. Simulations are presented to demonstrate the accuracies and effectiveness of the proposed methodologies.
An automatic sleep level estimation method was developed for monitoring and regulation of day time nap sleep. The recorded nap data is separated into continuous 5-second segments. Features are extracted from EEGs, EOG...
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ISBN:
(纸本)9781457702150
An automatic sleep level estimation method was developed for monitoring and regulation of day time nap sleep. The recorded nap data is separated into continuous 5-second segments. Features are extracted from EEGs, EOGs and EMG. A parameter of sleep level is defined which is estimated based on the conditional probability of sleep stages. An exponential smoothing method is applied for the estimated sleep level. There were totally 12 healthy subjects, with an averaged age of 22 yeas old, participated into the experimental work. Comparing with sleep stage determination, the presented sleep level estimation method showed better performance for nap sleep interpretation. Real time monitoring and regulation of nap is realizable based on the developed technique.
Quantum neural computing has nowadays attracted much attention, and tends to be a candidate to improve the computational efficiency of neural networks. In this paper, a new quantum neural network (QNN) is proposed bas...
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In this paper, we consider fast and desired consensus of directed network via pinning control. First, we provide a sufficient condition for the stability of desired consensus algorithm. Second, we investigate the prob...
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In this paper, we consider fast and desired consensus of directed network via pinning control. First, we provide a sufficient condition for the stability of desired consensus algorithm. Second, we investigate the problem of selecting optimal pinned nodes for driving fastest consensus, which is formulated as an Mixed-Integer Semidefinite Program. Finally, we illustrate all the results by simulating on some typical directed networks.
作者:
Keran ChenTian ZhouBoping LiuState Key Laboratory of Chemical Engineering
East China University of Science and Technology Meilong Road 130 Shanghai 200237 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Meilong Road 130 Shanghai 200237
作者:
Yu CheZhou TianBoping LiuState Key Laboratory of Chemical Engineering
East China University of Science and Technology Meilong Road 130 Shanghai 200237 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 P.R.China
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