BoostEMD: An Extension of EMD Method and Its Application for Denoising of EMG Signals

Damaševičius, Robertas; Vasiljevas, Mindaugas; Martišius, Ignas; Jusas, Vacius; Birvinskas, Darius; Woźniak, Marcin

doi:10.5755/j01.eee.21.6.13763

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Since the EPOC headset is not intended for finer signal detection, the electrodes pick up a lot of noise. Several techniques can be used to increase the Signal-to-Noise Ratio (SNR) such as band-pass filtering, averaging or class adaptive denoising [ 74 ], DCT compression [ 75 ], signal decomposition and thresholding [ 76 ], or nonlinear signal operators [ 77 ].…”

Section: Methodsmentioning

confidence: 99%

A Prototype SSVEP Based Real Time BCI Gaming System

Martišius

Damaševičius

2016

Computational Intelligence and Neuroscience

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Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

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Section: Methodsmentioning

confidence: 99%

A Prototype SSVEP Based Real Time BCI Gaming System

Martišius

Damaševičius

2016

Computational Intelligence and Neuroscience

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“…A further extension of the EMD method called BoostEMD was design by [105].This variant of the EMD method allows for use of higher orders IMFs. The authors conducted a comparative study with the classic EMD for classification accuracy and found their method was more effective.…”

Section: Denoising After Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals : A Review

Boyer¹,

Bouyer²,

Roy³

et al. 2023

Preprint

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EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

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“…However, these signal processing methods are not suitable for analyzing the nonstationary underwater acoustic signals, the feature extraction results cannot reflect the real features for the target signal well [ 8 , 9 ]. As the rapid development of signal processing technology, some signal processing methods for nonlinear and nonstationary signals are proposed, such as empirical mode decomposition (EMD) [ 10 , 11 ], local mean decomposition (LMD) [ 12 , 13 ], variational mode decomposition (VMD) [ 14 , 15 ], and their improved algorithms [ 16 , 17 , 18 , 19 ]. Some of these mode decomposition algorithms have been applied to feature extraction of underwater acoustic target signals, which can be divided into three groups based on the extracted feature information: energy feature extraction, complexity feature extraction, and frequency feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy

Wang

et al. 2019

Entropy

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Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.

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BoostEMD: An Extension of EMD Method and Its Application for Denoising of EMG Signals

Cited by 7 publications

References 3 publications

A Prototype SSVEP Based Real Time BCI Gaming System

A Prototype SSVEP Based Real Time BCI Gaming System

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals : A Review

A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy

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