Tensor based Blind Source Separation for Current Source Density Analysis of evoked potentials from somatosensory cortex of mice

Costa, João Paulo; Miranda, Ricardo Kehrle; Zanatta, Mateus da Rosa

doi:10.1109/ner.2017.8008438

Cited by 4 publications

(6 citation statements)

References 18 publications

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“…Many new technologies are introduced into BSS research. For example, signal sparse component analysis [2,3], dictionary learning [4,5], nonnegative matrix factorization [6,7], bounded component analysis [8,9], tensor decomposition [10,11], and machine learning [12]. However, these algorithms are sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

A New Blind Source Separation Algorithm Framework for Noisy Mixing Model Based on the Energy Concentration Characteristic in Signal Transform Domain

2021

Mathematical Problems in Engineering

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Blind source separation is a widely used technique to analyze multichannel data. In most real-world applications, noise is inevitable and will affect the quality of signal separation and even make signal separation failure. In this paper, a new signal processing framework is proposed to separate noisy mixing sources. It is composed of two different stages. The first step is to process the mixing signal by a certain signal transform method to make the expected signals have energy concentration characteristics in the transform domain. The second stage is formed by a certain BSS algorithm estimating the demixing matrix in the transform domain. In the energy concentration segment, the SNR can reach a high level so that the demixing matrix can be estimated accurately. The analysis process of the proposed algorithm framework is analyzed by taking the wavelet transform as an example. Numerical experiments demonstrate the efficiency of the proposed algorithm to estimate the mixing matrix in comparison with well-known algorithms.

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Section: Introductionmentioning

confidence: 99%

A New Blind Source Separation Algorithm Framework for Noisy Mixing Model Based on the Energy Concentration Characteristic in Signal Transform Domain

2021

Mathematical Problems in Engineering

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“…Finally, the servomotor operates the servomotors by applying the necessary rotation to move the servomotors. Independent Component Analysis (ICA) [REF8], Spatiotemporal Independent Component Analysis (stICA) [REF9], and more recently the use of a real-valued Parallel Factor Analysis (PARAFAC) approximation [REF10].…”

Section: Attachment a The Double-arc Positionermentioning

confidence: 99%

“…Thus, BSS techniques can assist the analysis of such signals. In order to verify the possibility to use multi-way techniques [REF10] proposed an approximation of the real-valued PARAFAC for complex-valued data. In this sense, we propose an improvement in the model created by [REF10] by using Time-Scale Transformation (Wavelets) to study one dimensional cortical recordings.…”

Section: Attachment a The Double-arc Positionermentioning

confidence: 99%

“…In order to verify the possibility to use multi-way techniques [REF10] proposed an approximation of the real-valued PARAFAC for complex-valued data. In this sense, we propose an improvement in the model created by [REF10] by using Time-Scale Transformation (Wavelets) to study one dimensional cortical recordings. As discussed in [REF11], wavelets have advantages over traditional linear Time-Frequency Analysis (TFA), shuch Short-Time Fourir Transform (STFT), because of its flexibility which allows us to model each wavelet according to signal propoerties.…”

Section: Attachment a The Double-arc Positionermentioning

confidence: 99%

“…The proposed approach applied either stICA and PARAFAC after performing CSD analysis as a preprocessing. However, the approach previously proposed in [REF10] considered the voltage time courses from -0.005s to 0.095s.…”

Section: Kronecker Productmentioning

confidence: 99%

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Zanatta¹

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Gostaria de primeiramente agradecer à meus pais pelo apoio e conselhos dados durante o decorrer deste mestrado. Também, agradecer meu orientador de Mestrado, Dr.-Ing. João Paulo Lustosa, por sua liderança e orientação, além das oportunidades que este me proporcionou ao me indicar como aluno visitante na TU Ilmenau. Também gostaria de agradecer meu coorientador de Mestrado, Dr.-Ing Ricardo Kehrle Miranda, que dedicou seu tempo à acompanhar meu desenvolvimento como pesquisador juntamente à meu orientador. i Dedicatória Aos meus pais Mario e Luciane, que dignamente me apresentaram à importância da família e ao caminho da honestidade e persistência.

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Robust tensor-based techniques for antenna array-based GNSS receivers in scenarios with highly correlated multipath components

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et al. 2020

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Tensor based Blind Source Separation for Current Source Density Analysis of evoked potentials from somatosensory cortex of mice

Cited by 4 publications

References 18 publications

A New Blind Source Separation Algorithm Framework for Noisy Mixing Model Based on the Energy Concentration Characteristic in Signal Transform Domain

A New Blind Source Separation Algorithm Framework for Noisy Mixing Model Based on the Energy Concentration Characteristic in Signal Transform Domain

Tensor-based time-delay estimation techniques for third generation global positioning system

Robust tensor-based techniques for antenna array-based GNSS receivers in scenarios with highly correlated multipath components

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