Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component From Nonstationary Mixtures—Algorithms

Koldovsky, Zbynek; Kautský, Václav; Tichavský, Petr

doi:10.1109/tsp.2022.3216106

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…Fast-ICA aimed to find an optimal direction

which makes the non-Gaussian property of this direction maximize:

( Qiu et al, 2022 ; Koldovsk et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…The independent sources are extracted sequentially, and the fixed- Frontiers in Bioengineering and Biotechnology frontiersin.org point iterative optimization algorithm is adopted to make the convergence of the results faster and more robust. Fast-ICA aimed to find an optimal direction w which makes the non-Gaussian property of this direction maximize: max J(w T x) (Qiu et al, 2022;Koldovsk et al, 2022).…”

Section: Improved Fast-icamentioning

confidence: 99%

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Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Lu,

Gong,

Xue

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method.Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination.Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

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“…Fast-ICA aimed to find an optimal direction

which makes the non-Gaussian property of this direction maximize:

( Qiu et al, 2022 ; Koldovsk et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Improved Fast-icamentioning

confidence: 99%

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Lu,

Gong,

Xue

et al. 2024

Front. Bioeng. Biotechnol.

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show abstract

“…We consider a similar statistical model of signals as in [7,10]. In particular, s(n) and z(n) are assumed to be mutually independent; the samples of s(n) as well as of z(n) are assumed to be independently distributed; let their pdfs depend on t and be denoted ps t and pz t , respectively; let st and zt symbolize random variables having the corresponding pdfs.…”

Section: Statistical Modelmentioning

confidence: 99%

“…The result points to several advantageous properties over the original CSV. First, it is the effective use of the non-stationarity of the mixing model and of the non-stationarity of the SOI (so-called double non-stationarity [10]) causing the achievable ISR to be lower than the original CSV [11]. Second, it is the fact that the model is identifiable even when the SOI is circular Gaussian, which is not possible with CSV [11].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio

Cmejla

Koldovsky

Kautský

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cramér-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian.

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The Robust Communication Method Against Dependent Jamming Based on Convex Hull in Communication Countermeasures

Su,

Zhang,

Sha

et al. 2024

Wireless Pers Commun

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Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component From Nonstationary Mixtures—Algorithms

Cited by 6 publications

References 55 publications

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio

The Robust Communication Method Against Dependent Jamming Based on Convex Hull in Communication Countermeasures

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