scholarly journals An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1356
Author(s):  
Mingai Li ◽  
Ruotu Wang ◽  
Dongqin Xu
Keyword(s):  
Feature Extraction ◽  
Statistical Significance ◽  
Back Propagation ◽  
Extraction Methods ◽  
Back Propagation Neural Network ◽  
Fuzzy Entropy ◽  
Coarse Grained ◽  
Dynamic Methods ◽  
Feature Information ◽  
Public Dataset

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.

scholarly journals An Improved Refined Composite Multivariate Multiscale Fuzzy Entropy Method for MI-EEG Feature Extraction

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mingai Li ◽  
Ruotu Wang ◽  
Jinfu Yang ◽  
Lijuan Duan
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Impulse Noise ◽  
Fuzzy Entropy ◽  
Coarse Grained ◽  
Support Vector ◽  
Noise Interference ◽  
Median Filters ◽  
Filter Performance ◽  
Feature Information

Feature extraction of motor imagery electroencephalogram (MI-EEG) has shown good application prospects in the field of medical health. Also, multivariate entropy-based feature extraction methods have been gradually applied to analyze complex multichannel biomedical signals, such as EEG and electromyography. Compared with traditional multivariate entropies, refined composite multivariate multiscale fuzzy entropy (RCmvMFE) overcomes the defect of unstable entropy values caused by the scale factor increase and is beneficial towards obtaining richer feature information. However, the coarse-grained process of RCmvMFE is mean filtered, which weakens Gaussian noise and is powerless against random impulse noise interference. This yields poor quality feature information and low accuracy classification. In this paper, RCmvMFE is improved (IRCmvMFE) by using composite filters in the coarse-grained procedure to enhance filter performance. Median filters are employed to remove the impulse noise interference from multichannel MI-EEG signals, and these filtered MI-EEGs are further smoothed by the mean filters. The multiscale IRCmvMFEs are calculated for all channels of composite filtered MI-EEGs, forming a feature vector, and a support vector machine is used for pattern classification. Based on two public datasets with different motor imagery tasks, the recognition results of 10 × 10-fold cross-validation achieved 99.43% and 99.86%, respectively, and the statistical analysis of experimental results was completed, showing the effectiveness of IRCmvMFE, as well. The proposed IRCmvMFE-based feature extraction method is superior compared to entropy-based and traditional methods.

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

2010 ◽  
Vol 36 ◽  
pp. 68-74
Author(s):  
Chuan Jun Liao ◽  
Shuang Fu Suo ◽  
Wei Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Mechanical Seal ◽  
Mechanical Seals ◽  
Feature Extraction Method ◽  
Different Types ◽  
Ae Signal ◽  
Feature Information ◽  
Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

Multi-steps degradation process prediction for bearing based on improved back propagation neural network

2012 ◽  
Vol 227 (7) ◽  
pp. 1544-1553 ◽  
Author(s):  
Lin Mi ◽  
Wei Tan ◽  
Ran Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Principal Component ◽  
Degradation Process ◽  
Extraction Methods ◽  
Back Propagation Neural Network ◽  
Time Frequency ◽  
Process Prediction

Bearing degradation process prediction is extremely important in industry. This article proposed a new method to achieve multi-steps bearing degradation prediction based on an improved back propagation neural network model. Firstly, time domain and time–frequency domain features extraction methods are employed to extract the original features from the mass vibration signals. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis is used to merge the original features and reduce the dimension, the typical sensitive features can be extracted. Then, based on the extracted features, the improved three-layer back propagation neural network model is constructed and trained for multi-steps bearing degradation process prediction. The phase space construction method is used to determine the embedding dimension of the back propagation neural network model. An accelerated bearing run-to-failure experiment was carried out, the results proved the effectiveness of the methodology.

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

2020 ◽  
pp. 106480462092413 ◽  
Author(s):  
Fan Zhang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Extraction Methods ◽  
Scale Invariant ◽  
Feature Vectors ◽  
Accuracy And Precision ◽  
Scale Invariant Feature

With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.

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