Appropriate AR modeling for surface electromyogram signals and its application in hand activity classification

Studies of vibrissal tactile discrimination show that rats can use their vibrissae to distinguish objects differing only in surface texture. Here we propose to use an autoregressive (AR) parametric model to analyze the electrical nerve discharge when vibrissae are sweeping surfaces differing only in roughness. The results were compared with the control situation: vibrissae sweeping the air. The AR model order was evaluated in both theoretical and practical ways using the Akaike information criterion (AIC) and calculating the delta value from the experimental data. The value obtained was five and the parameters a1 and a2 show more clearly the differences between control and surface measurements. The results show that the AR model is a good tool to use in this procedure.

Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov–Smirnov test statistic—Part I: Compromised autoregressive modeling with the aid of hypothesis tests and simulation analysis

Load forecasting is essential for effective and stable power system planning and operation. Decision making related to power system operation is influenced by future's electric load patterns. In this paper, particle swarm optimization (PSO) based autoregressive (AR) model is presented for short-term hourly load forecasting. First of all, among several potential input candidates, relevant inputs that have high correlation with prediction model's output are selected. According to the number of selected inputs, the order of AR model is fixed. Finally, AR model's parameters are optimized using PSO that is a global optimization algorithm. To verify the performance, the proposed method is applied to two kinds of real world hourly load dataset in South Korea. The proposed method shows good prediction accuracy.

This paper proposes a body weight prediction method using auto regressive (AR) model and Fuzzy-AR model. First, we employ 6 persons body weight change data of 365 days. AR model predicts body weight of a day from these time-series data. We calculate an order of AR model for each person by Akaike's Information Criterion. In the experiment, we predicted body weight change of next day for those subjects. The AR model obtained 0.798 in correlation coefficient between predicted and truth values. Second, we propose a Fuzzy-AR model that predicts body weight of next p days from last p days, where p is the order of AR model. In this method, we propose a Fuzzy-AR model with the fuzzy membership function using last p days data. In the experiment, the Fuzzy-AR model obtained 0.558 in correlation coefficient on 2 subjects.

Autoregressive (AR) model is applied to model various types of data. For confidential data, data confusion is very important to protect the data from being known by other unauthorized parties. This paper aims to find data modeling with transformations in the AR model. In this AR model, the noise has a Laplace distribution. AR model parameters include order, coefficients, and variance of the noise. The estimation of the AR model parameter is proposed in a Bayesian method by using the reversible jump Markov Chain Monte Carlo (MCMC) algorithm. This paper shows that the posterior distribution of AR model parameters has a complicated equation, so the Bayes estimator cannot be determined analytically. Bayes estimators for AR model parameters are calculated using the reversible jump MCMC algorithm. This algorithm was validated through a simulation study. This algorithm can accurately estimate the parameters of the transformed AR model with Laplacian noise. This algorithm produces an AR model that satisfies the stationary conditions. The novelty in this paper is the use of transformations in the Laplacian AR model to secure research data when the research results are published in a scientific journal. As an example application, the Laplacian AR model was used to model CO₂ emission data. The results of this paper can be applied to modeling and forecasting confidential data in various sectors.

This paper developed a denoising method termed f-x empirical-mode decomposition (EMD) predictive filtering. In this new method, we first applied EMD to each frequency slice in the f-x domain and obtained several intrinsic mode functions (IMFs). Then, an autoregressive model was applied to the sum of the first few IMFs to predict the useful steeper events. Finally, the predicted events were added to the sum of the remaining IMFs. This process improved the prediction precision by using an EMD-based dip filter to reduce the dip components before f-x predictive filtering. A synthetic data example is provided to show the performance of presented method. Introduction Random noise attenuation played an important role in seismic signal processing. Canales (1984) first uses f-x predictive filtering to attenuate random noise [1]. Since then, continuous efforts have been made to improve the predictive precision or to modify the conventional version to meet better the requirements set by various applications [2][3]. When the subsurface is extremely complex, f-x predictive filtering doesn’t yield good results because of the large number of dip components that need to be predicted. Huang et al. propose a new signal processing method that uses empirical mode decomposition (EMD) to prepare stable input for the Hilbert transform. The essence of EMD is to stabilize a nonstationary signal. That is, to decompose a signal into a series of intrinsic mode functions (IMFs) [4]. Each IMF has a relatively local constant frequency. The frequency of each IMF decreases according to the separation sequence of each IMF. EMD is a breakthrough in the analysis of linear and stable spectra. It adaptively separates nonlinear and nonstationary signals, which are features of seismic data, into different frequency ranges. Bekara and van der Baan apply f-x EMD to attenuation of random and coherent noise with good results [5]. For the purpose of random noise attenuation, the f-x domain EMD method can only be applied on NMO-corrected or poststack seismic data. With profiles containing dipping events, these methods will suppress some of the useful energy. In this paper, we present a new approach, termed f-x empirical mode decomposition predictive filtering (EMDPF) that combines f-x EMD and f-x predictive filtering. This new noise attenuation methodology can adapt to more complex seismic profiles than f-x EMD, and preserve more useful energy than f-x predictive filtering. The f-x EMDPF uses an EMD-based dip filter to reduce the dip components for the subsequent predictive filtering to improve the predictive precision. Predictive Filtering in Frequency-Space Domain Let s(t, h) (h=1,2,...,H) be the signal of trace and h and H be the number of traces. If the slope of a linear event with constant amplitude in a seismic section is λ, then ) 1 , ( ) 1 , ( x h t s h t s ∆ λ − = + , (1) where x ∆ denotes the trace interval. Eq. 1 an be transformed into the frequency domain to give x fh i e f s h f s ∆ λ π 2 ) 1 , ( ) 1 , ( − = + . (2) International Conference on Applied Science and Engineering Innovation (ASEI 2015) © 2015. The authors Published by Atlantis Press 16 For a specific frequency f0, from Eq. 2, we can obtain a linear recursion that is given by ) , ( ) 1 , ( ) 1 , ( 0 0 0 h f s f a h f s = + , (3) where x f i e f a ∆ λ π 0 2 0 ) 1 , ( − = . This recursion is a first order difference equation, also known as an autoregressive (AR) model of order 1. Similarly, superposition of p linear events in the t-x domain can be represented by an AR model of order p as the following equation: ) 1 , ( ) , ( ) 1 , ( ) 2 , ( ) , ( ) 1 , ( ) 1 , ( 0 0 0 0 0 0 0 p h f s p f a h f s f a h f s f a h f s − + + + − + = +  , (4) where ) , , 2 , 1 )( , ( 0 p h h f a  = denotes the predictive error filter, with a length of p. The prediction error energy ) ( 0 f E is given by the following equation:

A new robust time variant spectral estimation method is proposed. We use the parametric autoregressive (AR) model to obtain the desired spectra. For robust estimation, we assumed that the residual signal is identically and independently distributed. The probability density function (PDF) of the residual signal is a t-distribution with small /spl alpha/ degrees of freedom. We put a certain base function to the parameter of the AR model, so that the obtained spectra is time variant within the considered window. Simulation results show that by using a small /spl alpha/, the obtained running spectra is closer to the ideal spectra than that by using a large /spl alpha/. The mean square error (MSE) between the estimation result and the ideal spectra derived by using a small /spl alpha/ is smaller than that by utilizing a large /spl alpha/.

Publication in the conference proceedings of EUSIPCO, Trieste, Italy, 1996

The purpose of this study is to identifying time series analysis and mathematical model fitting on electroencephalography channels that are placed on Amyotrophic Lateral Sclerosis (ALS) patients with P300 based brain-computer interface (BCI). Amyotrophic Lateral Sclerosis (ALS) or motor neuron diseases are a rapidly progressing neurological disease that attacks and kills neurons responsible for controlling voluntary muscles. There is no cure and treatment effective to reverse, to halt the disease progression. A Brain- Computer Interface enables disable person to communicate & interact with each other and with the environment. To bypass the important motor difficulties present in ALS patient, BCI is useful. An input for BCI system is patient's brain signals and these signals are converted into external operations, for brain signals detection, Electroencephalography (EEG) is normally used. P300 based BCI is used to record the reading of EEG brain signals with the help of non-invasive placement of channels. In EEG, channel analysis Autoregressive (AR) model is a widely used. In the present study, Yule-Walker approach of AR model has been used for channel data fitting. Model fitting as a form of digitization is majorly required for good understanding of the dataset, and also for data prediction. Fourth order of the mathematical curve for this dataset is selected. The reason is the high accuracy obtained in the 4th order of Autoregressive model (97.51±0.64). In proposed Auto Regressive (AR) model has been used for Amyotrophic Lateral Sclerosis (ALS) patient data analysis. The 4th order of Yule Walker auto-regressive model is giving best fitting on this problem.

— In this paper, a second order autoregressive (AR) model is proposed to discriminate alcoholics using single trial gamma band Visual Evoked Potential (VEP) signals using 3 different classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN), Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear Discriminant (LD). Electroencephalogram (EEG) signals were recorded from alcoholic and control subjects during the presentation of visuals from Snodgrass and Vanderwart picture set. Single trial VEP signals were extracted from EEG signals using Elliptic filtering in the gamma band spectral range. A second order AR model was used as gamma band VEP exhibits pseudo-periodic behaviour and second order AR is optimal to represent this behaviour. This circumvents the requirement of having to use some criteria to choose the correct order. The averaged discrimination errors of 2.6%, 2.8% and 11.9% were given by LD, MLP-BP and SFA classifiers. The high LD discrimination results show the validity of the proposed method to discriminate between alcoholic subjects.

In 1979, Dickey and Fuller introduced a stationary test on the first order autoregressive model, AR(1), and limitting distribution of the estimator of autoregressive coefficient and the test statistics. The method has been applied to test the stationarity of the first order autoregressive time series model. However, the method has been applied regardless of sampling errors which usually occurs in data collection. autoregressive model subject to sampling errors.

This paper proposes a body weight prediction method using Fuzzy prediction model. Fuzzy prediction model is constructed by an autoregressive (AR) model based on body weight data and linear prediction models based on biological data. The biological data are obtained by pedometers such as number of steps, calorie consumption and so on. The Fuzzy prediction model is fixed by solving Yule-Walker equation and minimizing the Akaike's Information Criterion. In our experiment, the model predicts body weight change for next p days where p is the order of AR model. Then, four linear prediction models related to the biological data are constructed by linear regression analysis. We make a fuzzy membership function based on mean absolute error between body weight data and predicted value of each prediction model. Furthermore, these models are optimized for each subject in prediction models which add the biological data to AR model based on the mean absolute error. We employed 452 volunteers, and collected their body weight time-series data and the biological data during 730 days. We use these data from 1st to 365th day as learning data to determine the Fuzzy prediction model. As the result, the Fuzzy prediction model obtained higher correlation coefficient between predicted and truth values than the AR model on most subjects. In addition, the Fuzzy prediction model obtained smaller mean absolute prediction error than the AR model.

Dealing with issues related to safety of Nuclear Power Plants (NPPs) is of high importance and priority for assuring nonstop energy production. To enhance safety, modernization and upgrade of the aging installations by incorporating automation processes is unavoidable for many reasons, among them, environmental protection and lifetime extension of currently operating NPP. In that context, Human Machine Interface (HMI) applications are a subject of thorough study. The aim of this work is to develop a mechanism to evaluate the efficiency of HMI in nuclear power plant safety. To that end, HMI applications are addressed as a single joint system and they are not seen separately as a human and machine part. The proposed evaluator was implemented by utilizing Fuzzy Logic and holistic approaches. The implementation and all the experiments were conducted in Matlab by using the fuzzy toolbox.

A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.

IntroductionAnalysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform “prewhitening” to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates.MethodsIn this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI.ResultsWe find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives.ConclusionOur analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects.