Compressive Sensing of Multichannel EEG Signals via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> Norm and Schatten-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math> Norm Regularization

Zhu, Jun; Chen, Changwei; Su, Shoubao; Chang, Zinan

doi:10.1155/2016/2189563

Cited by 11 publications

(15 citation statements)

References 25 publications

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“…Instead of using frobenious norm, which is basically square root of the sum of the absolute squares defined in Eq 11, we have used the schattern-p norm. Schattern-p norm has been successfully used for sparse synthesis model and shows accurate results [3,7,18,33]. Eq 17 can be re-written asẑ…”

Section: Proposed Methodsmentioning

confidence: 99%

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Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals

et al. 2020

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Wearable electronics capable of recording and transmitting biosignals can provide convenient and pervasive health monitoring. A typical EEG recording produces large amount of data. Conventional compression methods cannot compress date below Nyquist rate, thus resulting in large amount of data even after compression. This needs large storage and hence long transmission time. Compressed sensing has proposed solution to this problem and given a way to compress data below Nyquist rate. In this paper, double temporal sparsity based reconstruction algorithm has been applied for the recovery of compressively sampled EEG data. The results are further improved by modifying the double temporal sparsity based reconstruction algorithm using schattern-p norm along with decorrelation transformation of EEG data before processing. The proposed modified double temporal sparsity based reconstruction algorithm out-perform block sparse bayesian learning and Rackness based compressed sensing algorithms in terms of SNDR and NMSE. Simulation results further show that the proposed algorithm has better convergence rate and less execution time.

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Section: Proposed Methodsmentioning

confidence: 99%

“…Angshul [10], illustrated CS recovery of EEG using 2-D fourier transform, however [38], claimed that better reconstruction can be achieved using wavelet domain instead of Gabor domain. Jun [3], claimed that using daubechies wavelets, the reconstruction accuracy achieved is better than that of other basis functions.…”

Section: Introductionmentioning

confidence: 99%

Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals

et al. 2020

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“…The work proposed by M. Fira et al [54] proposed a data-driven dictionary design by building the dictionary using the EEG signal from the training dataset itself rather than using any fixed dictionary. Furthermore, apart from these mainstream dictionaries for EEG signal, few other studies also used B-Spline dictionary [55,56], linear and cubic-Spline dictionaries [57], Spline dictionary [58], Meyer wavelet function dictionary [59], or Daubechies wavelets function dictionary [60] to obtain the sparse representation of the input signal.…”

Section: Sparse Representationmentioning

confidence: 99%

Trends in Compressive Sensing for EEG Signal Processing Applications

Gurve

Delisle-Rodríguez

Bastos

et al. 2020

Sensors

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The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain–computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.

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“…Recently, a simultaneous cosparsity and a low-rank optimization problem [39] have shown usefulness in the processing of EEG signals [40]. The idea of cosparsity has several advantages in the processing of multivariate signals.…”

Section: E Proposed Approach In Analysis Of Multivariate Signalsmentioning

confidence: 99%

“…As proposed in this paper, to apply the approach to process multivariate signals, the considered optimization models (22, 24 and 26) have to be reformulated. In some cases for multivariate signals analysis, instead of norms 1 and 2 , we should use Schatten-p norm [39], [40]. VOLUME 4, 2016…”

Section: E Proposed Approach In Analysis Of Multivariate Signalsmentioning

confidence: 99%

Sparse Reconstruction for Enhancement of the Empirical Mode Decomposition-Based Signal Denoising

Brzostowski

2020

IEEE Access

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Effective signal denoising methods are essential for science and engineering. In general, denoising algorithms may be either linear or non-linear. Most of the linear ones are unable to remove the noise from the real-world measurements. More suitable methods are usually based on non-linear approaches. One of the possible algorithms to signal denoising is based on empirical mode decomposition. The typical approach to the empirical mode decomposition-based signal denoising is the partial reconstruction. More recently, a new concept inspired by the wavelet thresholding principle was proposed. The method is named the interval thresholding. In this article, we further extend the concept by the application of the sparse reconstruction to the empirical mode decomposition-based signal denoising algorithm. To this end, we state and then solve the problem of signal denoising as a regularization problem. In the article, we consider three cases, that is, three types of penalty functions. The first algorithm is combining total variation denoising with empirical mode decomposition approach. In the second one, we applied the fused LASSO Signal Approximator to design the empirical mode decomposition-based signal denoising algorithm. The third approach solves the denoising problem by applying a non-convex sparse regularization. The proposed algorithms were validated on synthetic and real-world signals. We found that the proposed methods have the ability to improve the accuracy of the signal denoising in comparison to the reference methods. Significant improvements from both the synthetic and the real-world signals were obtained for the algorithm based on non-convex sparse regularization. The presented results show that the proposed approach to signal denoising based on empirical mode decomposition algorithm and sparse regularization gives a great improvement of accuracy, and it is the promising direction of future research. INDEX TERMS non-linear signal processing, non-convex optimization, gyroscopes.

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Compressive Sensing of Multichannel EEG Signals via lq Norm and Schatten-p Norm Regularization

Cited by 11 publications

References 25 publications

Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals

Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals

Trends in Compressive Sensing for EEG Signal Processing Applications

Sparse Reconstruction for Enhancement of the Empirical Mode Decomposition-Based Signal Denoising

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