Blind Source Separation by Multiresolution Analysis using AMUSE Algorithm

Oliveira, Bruno Rodrigues de; Duarte, Marco Aparecido Queiroz; Filho, Jozué Vieira

doi:10.33837/msj.v1i3.106

Cited by 3 publications

(6 citation statements)

References 9 publications

(6 reference statements)

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“…In 2018, Oliveira et al proposed the use of multi-resolution analysis in the three resolution ranks of the wavelet transform, and then separated the source signal using the AMUSE algorithm with different resolution ranks. The results were better than those obtained when using the original AMUSE method alone to estimate the signal [24]. In 2019, Chua and Klejin proposed a low-delay method for BSS.…”

Section: Related Workmentioning

confidence: 89%

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Pan¹,

Li²,

Wang³

et al. 2022

ITC

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Electrical Impedance Tomography (EIT) can perform non-invasive, low-cost, safe, fast, and simple system structure and functional imaging to map the distribution and changes of root zone. Multi frequency EIT solves the problem that single-frequency EIT can only carry more impedance information than a given single excitation frequency. It still remains challenges to simultaneously obtain multi-frequency electrical impedance tomography. To address the problem, a mixed signal superimposed by multiple frequencies is injected to the object. Essentially, separating the measured mixed voltage signals, which can be used to obtain electrical impedance information at different frequencies at the same time quickly. Since the measurement signal is a multi-frequency signal, the effect of decomposing the multi-frequency signal directly affects the accuracy of imaging. In order to obtain more accurate data, this article used the variational mode decomposition (VMD) method to decompose the measured multi-frequency signal. Accurate amplitude and phase information could be obtained simultaneously at the same time in multi-frequency excitation, and these data could be used to reconstruct electrical impedance distribution The results showed that the proposed method can achieve the expected imaging effect. It was concluded that using the variational modal decomposition method to process the data of multi-frequency signals is more accurate and the imaging effect is better, and it can be applied to multi-frequency electrical impedance imaging in practice.

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Section: Related Workmentioning

confidence: 89%

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Pan¹,

Li²,

Wang³

et al. 2022

ITC

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show abstract

“…classical approach, the spatial filtering [9] and decomposition technique such as wavelet filter Bank approach and Empirical Mode decomposition [13], [26] approach has also been used to a large extend . Furthermore, blind source separation (BSS) [10][11][12] is another efficient and extensively used technique to minimize the artifacts and to separate the source signal from the recorded EEG signal…”

Section: Eeg Signalmentioning

confidence: 99%

“…After the literature survey, it has been found that various spatial filtering has been employed before the feature extraction from the multichannel SSVEP BCI system.The features extraction from the multichannel recordings directly influences the detection accuracy because the multichannel EEG signals are correlated [8]. Blind source separation (BSS) is an advanced signal processing method applied to get the independent sources mixed at the sensor point or decorrelate the recorded EEG signal [21][22][23]. The performance of BSS, along with CCA, is a new method for detecting SSVEP from the multichannel EEG signals.…”

Section: Eeg Signalmentioning

confidence: 99%

“…The AMUSE algorithm uses straightforward principle, where the estimated components are spatio-temporally uncorrelated and less complex, where the components are arranged according to the reducing singular value of covariance matrix [21][22]. The AMUSE algorithm is performed into two steps using the principle component analysis.…”

Section: Source Signal Recorded Signal Estimated Signalmentioning

confidence: 99%

“…S is the diagonal of decreasing singular value, and U and V are the Eigenvector matrices. The separating matrix is estimated as follows [21]:…”

Section: Step-2: Estimation Of Separating Matrix W With Svd [20-21]mentioning

confidence: 99%

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Detection of Target Frequency from Multichannel SSVEP Based BCI System using combined approach of BSS-CCA

Ojha¹,

Sharma

Mukul

2023

Preprint

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This paper demonstrate the combined approach of Blind Source Separation (BSS) and canonical correlation analysis (CCA) to detect the frequency component of Steady state Visual evoked Potential (SSVEP) based Brain computer Interface (BCI) system from non-invasive recorded electroencephalography (EEG) signal. Detection of SSVEP frequency component with great accuracy is most challenging and difficult task for the development of SSVEP based BCI system. Canonical correlation analysis (CCA) is the most widely and rigorously employed method to detect the SSVEP frequency component from multichannel recorded EEG signal. But the presence of spontaneous EEG signal and artifacts that often occurs during the recording of scalp-based EEG signal may deteriorate the detection accuracy of SSVEP frequency component from recorded EEG signal. This work investigates the blind source separation (BSS) as preprocessing technique to decorrelate the source signal (SSVEP) from the recorded, mixed-signal (EEG) to improve the detection accuracy of SSVEP based BCI Inference system. In this paper, the author proposes second-order AMUSE-based BSS algorithms as preprocessing methods for multichannel EEG signals. The CCA technique employs the preprocessed signal to detect the SSVEP frequency components from the recorded EEG signal. The obtained finding indicates that the proposed BSS-CCA method significantly improved the SSVEP detection accuracy as compared to the standard CCA method. In addition to this, the author has also reported that selection of stimulus frequency also plays a vital role in order to improve the detection accuracy of SSVEP BCI system. The obtained result indicate that average detection is much more improved using when stimulus frequency is the range of alpha band (8Hz – 16Hz) as compared to stimulus frequency beyond alpha band (above 16Hz) using both CCA and BSS-CCA approach.

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Application of Blind Signal Separation Methods in the Problems on Improving the Interference Immunity of Space Communication Systems with Quadrature Amplitude Modulation

Tyapkin,

Vazhenin,

Plokhikh

2023

Russ. Aeronaut.

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Blind Source Separation by Multiresolution Analysis using AMUSE Algorithm

Cited by 3 publications

References 9 publications

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Detection of Target Frequency from Multichannel SSVEP Based BCI System using combined approach of BSS-CCA

Application of Blind Signal Separation Methods in the Problems on Improving the Interference Immunity of Space Communication Systems with Quadrature Amplitude Modulation

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