Research on EEG Identification Computing Based on Photo Images

2011 ◽  
Vol 217-218 ◽  
pp. 1366-1371 ◽  
Author(s):  
Zhen Dong Mu ◽  
Jian Feng Hu
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Bp Neural Network ◽  
False Recognition ◽  
Recognition Rate ◽  
Ar Model ◽  
Eeg Signals ◽  
Error Recognition ◽  
Correct Recognition Rate ◽  
Identification Tool

In the information era, information security becomes more important, the use of EEG as identification tool become more and more. In this paper, we used the subjects’ photos as stimulation. In order to obtain the Identification classifier of different subjects, we used AR model to convert the EEG signals from time domain into the frequency domain, used Fisher’ distance to extract the feature. Finally, we calculated the feature by using BP neural network. Through the analysis of the correct recognition rate, error recognition rate and false recognition rate, we achieved the purpose of using EEG as identification tool.

Research of Classification Methods of EEG Signal Based on Wavelet Packet Transform and LVQ Neural

2014 ◽  
Vol 1049-1050 ◽  
pp. 1626-1630
Author(s):  
Xue Yan Pang ◽  
Shi Nin Yin ◽  
Hong Zhou Li ◽  
Jian Ming Zhu ◽  
Zhen Cheng Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Specific Frequency ◽  
Correct Recognition Rate ◽  
Electroencephalogram Eeg

In order to improve the correct recognition rate of EEG(Electroencephalogram,EEG) signals to meet the needs of Brain-Computer Interface system,this paper put forward a new method of signal recognition which combines wavelet packet decomposition and LVQ neural network.First,using the method of wavelet packet to analyze the signal,and then extract the specific frequency band’s energy of wavelet packet as characteristics.Then using the LVQ neural network model to study the distinguishing between the two EEG datas of Motor Imagery.The simulation experiment uses Matlab software to design LVQ neural network model to judge the two kinds of Motor Imagery task.In the process of judgment,respecti-vely to classify the data by using BP neural network and LVQ neural network.Experimental results show that the LVQ neural network can have a higher correct accuracy to recognize the motor imaginary task than BP neural.

Monitoring and analysis of athletes’ local body movement status based on BP neural network

2020 ◽  
pp. 1-11
Author(s):  
Ling Zhang ◽  
Faze Liang
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
The Body ◽  
Gradient Theory ◽  
Motion Recognition ◽  
Related Research ◽  
Network Algorithm ◽  
Neural Network Algorithm

At present, the body recognition detection of athletes is mostly technical recognition, and the detection of exercise state is less, and the related research is basically blank. Based on this, based on BP neural network algorithm, this study develops athletes’ motion capture based on wearable inertial sensors, and builds a wireless signal transmission scheme based on sensor system. At the same time, this paper constructs the coordinate system to complete the attitude angle settlement and motion recognition and combines the athlete’s actual situation to establish the athlete’s limb trajectory calculation model and analyzes the athletes’ movement patterns. In addition, this paper combines neural network algorithm to analyze, and builds a neural network based athlete body motion recognition model, and analyzes the model effectiveness through simulation system. Studies have shown that when using time domain features+trajectory features as neural network inputs, the hand recognition rate is somewhat improved compared to the use of only time domain features as neural network inputs. It can be seen that the algorithm model of this study has certain validity and can be used as a reference for subsequent related research gradient theory.

Machine-Printed Invoice Number Based on Fuzzy Recognition

2012 ◽  
Vol 214 ◽  
pp. 705-710 ◽  
Author(s):  
Xiao Ping Xian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real System ◽  
Recognition Method ◽  
Recognition Time ◽  
Recognition Ability ◽  
System Requirements ◽  
Fuzzy Recognition ◽  
Detection And Separation

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

The Recognition of Handwritten Digital Based on BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1239-1243
Author(s):  
Shan Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Practical Significance ◽  
Test Results ◽  
Recognition Method ◽  
Number Recognition ◽  
Recognition Ability ◽  
Fuzzy Recognition ◽  
Zip Code

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

Fault Detection of Carbide Anvil Based on Hurst Exponent and BP Neural Network

2013 ◽  
Vol 805-806 ◽  
pp. 1881-1886 ◽  
Author(s):  
Li Han ◽  
Bin Chen ◽  
Bao Cheng Gao ◽  
Zhao Li Yan ◽  
Xiao Bin Cheng
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Hurst Exponent ◽  
Recognition Rate ◽  
Pulse Signal ◽  
Characteristic Parameters ◽  
Threshold Method ◽  
Fault Recognition ◽  
Bp Neural Network Model

This paper proposed a novel diagnosis algorithm based on Hurst exponent and BP neural network to detect carbide anvil fault in synthetic diamond industry. Firstly, a sort of preprocessing algorithm is proposed, which uses the sliding window and energy threshold method to separate the pulse from initial continuous signal. Then, some characteristic parameters which are based on Hurst exponent are extracted from the separated pulse signal. These characteristic parameters are used to construct fault characteristic vectors. Finally, the BP neural network model was established for fault recognition. Experimental results show that the proposed fault detection method has high recognition rate of 96.7%.

An Optimized BP Neural Network Based on DS Evidential Reasoning on Heart Disease Prediction

2012 ◽  
Vol 263-266 ◽  
pp. 3342-3347
Author(s):  
Nan Nan Xie ◽  
Fei Yan Chen ◽  
Kuo Zhao ◽  
Liang Hu
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Evidential Reasoning ◽  
Self Organization ◽  
Uncertain Information ◽  
Data Set ◽  
Time Prediction ◽  
Minimum Points

BP neural network is a widely used neural network, with advantages as adaptability, fault tolerance and self-organization. However, BP neural network is difficult to determine the network structure, and easy to fall into local minimum points. In this paper, an optimized BP neural network was proposed based on DS, he advantages of DS Evidential Reasoning on uncertain information are used to improve the recognition rate and credibility of BP. Experiments on Heart Disease Data set shows the proposed method have good performance on run time, prediction accuracy and robustness.

SEMG Analysis Basing on AR Model and Bayes Taxonomy

2010 ◽  
Vol 44-47 ◽  
pp. 3355-3359 ◽  
Author(s):  
Guang Ying Yang ◽  
San Xiu Wang ◽  
Yue Chen
Keyword(s):  
Virtual Instrument ◽  
Recognition Rate ◽  
Ar Model ◽  
Semg Signal ◽  
Bayes Statistics ◽  
Emg Signal ◽  
Auto Regressive ◽  
Error Recognition ◽  
Ar Modeling ◽  
The Cost

This paper introduced a pattern recognition method based on auto-regression (AR) model and bayes taxonomy. The proposed methodology consists of three steps. In the first step, the paper designs a circuit to collect surface electromyography (SEMG) signal. In the second step, Auto-regressive (AR) modeling in time series has been applied on people’s forearm muscle. So, EMG signal is preprocessed using AR-Model to extract features from MES. After calculated the coefficients of and AR model, we distill the AR coefficients as its eigenvector. In the third step, a bayes statistics algorithm is designed to classify the muscle movement of forearm. This paper finds this method has many advantages such as reducing error recognition rate and has a relative good result. It proves that there are some relations between motion pattern and AR coefficients. At the same time, this paper adopts virtual instrument technology to raise accuracy of measurement, reduce the cost and workload.

Application BP Neural Network in the Speaker Recognition Based on Chaos Particle Swarm Optimization Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 2805-2808
Author(s):  
Guo Wen Wang ◽  
Shi Xin Luo ◽  
Li He ◽  
Gang Yin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Speaker Recognition ◽  
Particle Swarm Optimization Algorithm ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Bp Network ◽  
Swarm Optimization

According to the question that BP Neural Network has slow velocity of convergence and is apt to fall into the minimum value, chaos thought is adopted in the particle swarm optimization (PSO). For this, chaos particle swarm optimization algorithm, which improve the ability of getting rid of fractional extreme point in the PSO, is presented and applied to the BP network exercise so that the calculation accuracy and velocity of convergence of BP network are increased. The method of training the BP network for speaker recognition, the recognition rate and speed of training have been greatly improved, making the speaker recognition based on BP neural network to get better results.

Structural Damage Localization Based on AR Model and BP Neural Network

2011 ◽  
Vol 94-96 ◽  
pp. 1211-1215
Author(s):  
Yan Song Diao ◽  
Fei Yu ◽  
Dong Mei Meng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Ar Model ◽  
Model Parameters ◽  
Damage Localization ◽  
Modal Parameter ◽  
Damage Location ◽  
Simulation And Experiment ◽  
Damage Characteristic

When the AR model is used to identify the structural damage, one problem is often met, that is the method can only make a decision whether the structure is damaged, however, the damage location can not be identified exactly. A structural damage localization method based on AR model in combination with BP neural network is proposed in this paper. The AR time series models are used to describe the acceleration responses. The changes of the first 3-order AR model parameters are extracted and composed as damage characteristic vectors which are put into BP neural network to identify the damage location. The effectiveness of the method is validated by the results of numerical simulation and experiment for a four-layer offshore platform. Only the acceleration responses can be used adequately to localize the structural damage, without the usage of modal parameter and excitation force. Thus the dependence on the modal parameter and excitation can be avoided in this method.

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