Blind source separation (BSS) of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Ensemble empirical mode decomposition (EEMD)-based methods are commonly used for blind separation of single input multiple outputs. However, all of these EEMD-based methods appear in the edge effect problem when cubic spline interpolation is used to fit the upper and lower envelopes of the given signals. It is therefore imperative to have good methods to explore a more suitable design choice, which can avoid the problems mentioned above as much as possible. In this paper we present a novel single-mixture blind source separation method based on edge effect elimination of EEMD, principal component analysis (PCA) and independent component analysis (ICA). EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). In extreme point symmetry extension (EPSE), optimum values of endpoints are obtained by minimizing the deviation evaluation function of signal and signal envelope. Edge effect is turned away from signal by abandoning both ends’ extension parts of IMFs. PCA is applied to reduce dimensions of IMFs. ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction of IMFs. The separated performance of edge EPSE-EEMD-PCA-ICA algorithm is compared with EEMD-ICA and EEMD-PCA-ICA algorithms through simulations, and experimental results show that the former algorithm outperforms the two latter algorithms with higher correlation coefficient and lower relative root mean square error (RRMSE).

Edge Effect Elimination in Single-Mixture Blind Source Separation

We use nonlinear and non-stationary features of sEMG to identify various gestures.In order to reduce the complexity of EMD nonlinear MLE method is used.In order to improve characteristic stability we use a method based on nonlinear MLE.The proposed method classifies six different hand gestures up to 97.6% accuracy.The method can be used for prosthetics and other rehabilitation applications. Surface Electromyography (sEMG) is a non-invasive, easy to record signal of superficial muscles from the skin surface. The sEMG is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. Considering the nonlinear and non-stationary characteristics of sEMG, hand gesture recognition using sEMG signals necessitate designers to use Maximal Lyapunov Exponent (MLE) or ensemble Empirical Mode Decomposition (EMD) based MLEs. In this research, we propose a hand gesture recognition method of sEMG based on nonlinear multiscale MLE. The aim is to increase the classification accuracy of sEMG features while reducing the complexity of EMD. The nonlinear MLE features are classified using Flexible Neural Tree (FNT), which can solve highly structured dependent problems of the Artificial Neural Network (ANN). The testing has been conducted using several experiments with five participants. The classification performance of nonlinear multiscale MLE method is compared with MLE and EMD-based MLE through simulations. Experimental results demonstrate that the former algorithm outperforms the two latter algorithms and can classify six different hand gestures up to 97.6% accuracy.

Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification

Blind source separation of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Numerous blind source separation methods are commonly used for blind separation of single input multiple output. However, the priori knowledge of the signal is assumed to be known or the main channels selected from multi-channel output are not self-adaptive and automatic. Presented in this paper is a new method based on dimensionality reduction of ensemble empirical mode decomposition (EEMD), and ICA does not rely on such assumptions. The EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction IMFs. Principal component analysis (PCA) is applied to reduce dimensions of IMFs. The separated performance of EEMD-PCA-ICA algorithm is compared with EEMD-ICA through simulations, and experimental results show EEMD-PCA-ICA algorithm outperforms EEMD-ICA with higher cross-correlation and lower relative root mean squared error (RRMSE).

Single-Mixture Source Separation Using Dimensionality Reduction of Ensemble Empirical Mode Decomposition and Independent Component Analysis

Usually, bearing faults are diagnosed by the search of bearing characteristic frequencies in the spectrum of current or vibration signals. This local approach, even efficient, has the drawback of requiring the a prior knowledge of these frequencies. Moreover, characteristic bearing frequencies are only a part of the global spectral signature induced by natural bearing damages. In real situations, a fault on a particular bearing element may not produce the corresponding characteristic frequency. Several multiple harmonics of this frequency and sidebands related to their modulations by rotational frequencies can be quite dominant. An effective diagnosis should rather consider the global fault signature. Based on the fact that the global information encoded in the frequency domain is usually descriptive enough to diagnose and classify bearing faults, the present work proposes a classification scheme for bearing conditions which does not require the characteristic frequencies to be known or estimated. The method combines the envelope analysis, the sliding Fast Fourier Transform (FFT) technique and Principal Component Analysis (PCA). The application on experimental data shows that bearing faults can be diagnosed and classified accurately and without overlapping, irrespective of the system operating point. The extracted spectral features are informative enough to discriminate between different conditions of bearing.

A global approach for the classification of bearing faults conditions using spectral features

Surface Electromyography (sEMG) is widely used in evaluating the functional status of hands to assist in hand gesture recognition in many fields of treatment and rehabilitation. Multi-channel paral...

Hand gesture recognition system using single-mixture source separation and flexible neural trees

A method of bearing fault diagnosis and classification based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is presented There are aliasing phenomenon and correlation among intrinsic mode functions decomposed by EMD Through elimination of information redundancy, an estimated intrinsic mode functions that includes much information of fault is separated for fault diagnosis and classification by the method of ICA, in fault diagnosis, multi-signals are composed of one engineering signal sampled by single sensor by using time-delay technique, and a fault sub-signal for fault diagnosis is separated from multi-signals by the method of EMD-ICA In fault classification, based on fault sub-signal estimated by EMD-ICA, a parameter vector is composed of the coefficients of frequency, the residual of multi-information, correlation coefficients, entropy of frequency, approximate entropy and kurtosis, and this parameter vector is regarded as input of general regression neural network for judging bearing's three fault type

Fault diagnosis and classification for bearing based on EMD-ICA

Empirical Mode Decomposition (EMD) can analyze non-stationary signal[4] in time-frequency domain accurately, and be used widely. Virtual Instruments has made a very fast development, with the help of powerful modular hardware, combined efficient and flexible software, graphical LabVIEW[5] language can meet all the needs of test, measurement and automation, and can improve efficiency widely. In this paper, LabVIEW is be used to design the program of Hilbert Huang Transform (HHT), by analyzing EMD and instantaneous frequency, and has been proved by simulation that the program can run effectively. The program provides more choices for LabVIEW signal processing module.

Design of empirical mode decomposition and HHT system base on LabVIEW

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Fault diagnosis and classification for bearing based on EMD-ICA

Design of empirical mode decomposition and HHT system base on LabVIEW