scholarly journals Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 217 ◽  
Author(s):  
Izzat Aulia Akbar ◽  
Tomohiko Igasaki
Keyword(s):  
Support Vector Regression ◽  
Estimation Method ◽  
Electrode Site ◽  
Estimation Methods ◽  
Support Vector ◽  
Physical Damage ◽  
Prediction Ability ◽  
Estimation Performance ◽  
Eeg Data ◽  
Electroencephalogram Eeg

As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able to improve their estimation ability as the length of time or number of trials increases. Thus, in this study, we aim to find an effective drowsiness estimation method that is also able to improve its prediction ability as the subject’s activity increases. We used electroencephalogram (EEG) data to estimate drowsiness, and the Karolinska sleepiness scale (KSS) for drowsiness evaluation. Five parameters (α, β/α, (θ+α)/β, activity, and mobility) from the O1 electrode site were selected. By combining these parameters and KSS, we demonstrate that a typical support vector regression (SVR) algorithm can estimate drowsiness with a correlation coefficient (R2) of up to 0.64 and a root mean square error (RMSE) of up to 0.56. We propose a “recurrent SVR” (RSVR) method with improved estimation performance, as highlighted by an R2 value of up to 0.83, and an RMSE of up to 0.15. These results suggest that in addition to being able to estimate drowsiness based on EEG data, RSVR is able to improve its drowsiness estimation performance.

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

scholarly journals Projection wavelet weighted twin support vector regression for OFDM system channel estimation

2020 ◽  
Author(s):  
Lidong Wang ◽  
Yimei Ma ◽  
Xudong Chang ◽  
Chuang Gao ◽  
Qiang Qu ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Support Vector Regression ◽  
Orthogonal Frequency Division Multiplexing ◽  
Channel Model ◽  
Wireless Channel ◽  
Estimation Methods ◽  
Support Vector ◽  
Ofdm System ◽  
Twin Support Vector Regression ◽  
Empirical Risk

Abstract In this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.

scholarly journals A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 830 ◽  
Author(s):  
Zhengyu Liu ◽  
Jingjie Zhao ◽  
Hao Wang ◽  
Chao Yang
Keyword(s):  
Support Vector Regression ◽  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
Health Indicators ◽  
Support Vector ◽  
Health Indicator ◽  
De Algorithm ◽  
Key Factor ◽  
Charging And Discharging Processes

An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values.

scholarly journals Performance Degradation Prediction Based on a Gaussian Mixture Model and Optimized Support Vector Regression for an Aviation Piston Pump

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3854 ◽  
Author(s):  
Chuanqi Lu ◽  
Shaoping Wang
Keyword(s):  
Support Vector Regression ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Performance Degradation ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Information Quantity ◽  
Prediction Ability ◽  
Outlet Pressure

Performance degradation prediction plays a key role in realizing aviation pump health management and condition-based maintenance. Thus, this paper proposes a new approach that combines a Gaussian mixture model (GMM) and optimized support vector regression (SVR) to predict aviation pumps’ degradation processes based on the pump outlet pressure signals. Different from other feature extraction methods in which the information of intrinsic mode functions (IMFs) is not fully utilized, some useful IMF components are firstly chosen, and the corresponding multi-domain features are extracted from each selected component. Considering that it is not the case that all features are equally sensitive to degradation assessment, PCA is used to select more sensitive degradation features. Since the distribution of these extracted features is a stochastic process in feature space, meanwhile, self-information quantity can describe the uncertainty of system by measuring the average information quantity contained in the probability distribution, self-information quantity based on GMM is defined as degradation index (DI) to describe the degradation degree of the pump quantitatively. Finally, an SVR model is constructed to predict the degradation status of the pump. To achieve higher prediction accuracy, phase space reconstruction theory is first employed to determine the number of the inputs of the SVR model, then a new method combining particle swarm optimization (PSO) with grid search (GS) is developed to optimize the parameters of the SVR model. Finally, both the online data and historical data are utilized for the construction of the SVR model, respectively. The effectiveness of the proposed approach is validated by full life cycle data collected from an aviation pump test rig. The results demonstrate that the DI extracted from pump outlet pressure signals can effectively identify and track the current deterioration stage, and the established SVR model has better prediction ability when compared with previously published methods.

Highway Traffic Accident Prediction Based on SVR Trained by Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 5886-5889 ◽  
Author(s):  
Zhen Qi Yang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Regression Function ◽  
Support Vector ◽  
Highway Traffic ◽  
Prediction Ability ◽  
Accident Prediction ◽  
Better Than

The multi researches and experiments show that the future highway traffic accident situation is shown by the highway traffic accident prediction. In the paper, support vector regression trained by genetic algorithm is presented in highway traffic accident prediction. In the method, genetic algorithm is used to train the parameters of support vector regression. Firstly, the regression function of support vector regression algorithm is introduced, and the parameters of support vector regression are optimized by genetic algorithm. The computation results between G-SVR and SVR can indicate that the prediction ability for highway traffic accidents of G-SVR is better than that of SVR absolutely.

scholarly journals Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Xueqian Liu ◽  
Hongyi Yu
Keyword(s):  
Frequency Estimation ◽  
Additive White Gaussian Noise ◽  
Estimation Method ◽  
Threshold Value ◽  
Small Sample ◽  
Support Vector ◽  
Linear Filter ◽  
Ml Estimation ◽  
Single Tone ◽  
Estimation Performance

Maximum likelihood (ML) algorithm is the most common and effective parameter estimation method. However, when dealing with small sample and low signal-to-noise ratio (SNR), threshold effects are resulted and estimation performance degrades greatly. It is proved that support vector machine (SVM) is suitable for small sample. Consequently, we employ the linear relationship between least squares support vector regression (LS-SVR)’s inputs and outputs and regard LS-SVR process as a time-varying linear filter to increase input SNR of received signals and decrease the threshold value of mean square error (MSE) curve. Furthermore, it is verified that by taking single-tone sinusoidal frequency estimation, for example, and integrating data analysis and experimental validation, if LS-SVR’s parameters are set appropriately, not only can the LS-SVR process ensure the single-tone sinusoid and additive white Gaussian noise (AWGN) channel characteristics of original signals well, but it can also improves the frequency estimation performance. During experimental simulations, LS-SVR process is applied to two common and representative single-tone sinusoidal ML frequency estimation algorithms, the DFT-based frequency-domain periodogram (FDP) and phase-based Kay ones. And the threshold values of their MSE curves are decreased by 0.3 dB and 1.2 dB, respectively, which obviously exhibit the advantage of the proposed algorithm.

scholarly journals InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7251
Author(s):  
Hong Zeng ◽  
Jiaming Zhang ◽  
Wael Zakaria ◽  
Fabio Babiloni ◽  
Borghini Gianluca ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Support Vector ◽  
Learning Method ◽  
Learning Models ◽  
Eeg Signals ◽  
Eeg Data ◽  
State Detection ◽  
Fatigue Detection ◽  
Electroencephalogram Eeg ◽  
Machine Learning Models

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

scholarly journals Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

2020 ◽  
Vol 12 (11) ◽  
pp. 1903
Author(s):  
Cheng Hu ◽  
Shaoyang Kong ◽  
Rui Wang ◽  
Fan Zhang ◽  
Lianjun Wang
Keyword(s):  
Support Vector Regression ◽  
Cross Section ◽  
Radar Cross Section ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Feature Subset ◽  
Mass Estimation ◽  
Optimal Feature Subset ◽  
Optimal Feature

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

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