Compressive Sensing of Multichannel EEG Signals Based on Graph Fourier Transform and Cosparsity

Zou, Xiuming; Feng, Lei; Sun, Huaijiang

doi:10.1007/s11063-019-10150-5

Cited by 8 publications

(10 citation statements)

References 22 publications

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“…First, they obtained the s p norm along with the decorrelation transformation of EEG data, and then double temporal sparsity-based reconstruction algorithm has been applied for the signal reconstruction. Few other recent studies [81] have also shown that combining cosparsity and low-rank property usually results in efficient CS reconstruction of multichannel EEG signals. However, these studies rarely incorporated the effect of noise in their studies.…”

Section: Reconstruction Algorithmsmentioning

confidence: 92%

Trends in Compressive Sensing for EEG Signal Processing Applications

Gurve¹,

Delisle-Rodríguez²,

Bastos-Filho³

et al. 2022

Preprint

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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.

show abstract

Section: Reconstruction Algorithmsmentioning

confidence: 92%

Trends in Compressive Sensing for EEG Signal Processing Applications

Gurve¹,

Delisle-Rodríguez²,

Bastos-Filho³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This may also degrade the performance of the reconstruction method. In order to enforce inherent correlation across different channels and cosparsity of multichannel EEG signals, X. Zou et al [81] proposed a graph Fourier transform and nonconvex optimization (GFTN)-based method, which can exploit the accurate adjacent relationship between the real physical channels. Similar to [80], this work also used ADMM for signal reconstruction.…”

Section: Reconstruction Algorithmsmentioning

confidence: 99%

“…First, they obtained the

norm along with the decorrelation transformation of EEG data, and then double temporal sparsity-based reconstruction algorithm has been applied for the signal reconstruction. Few other recent studies [ 81 ] have also shown that combining cosparsity and low-rank property usually results in efficient CS reconstruction of multichannel EEG signals. However, these studies rarely incorporated the effect of noise in their studies.…”

Section: Cs For Eeg Signalmentioning

confidence: 99%

Trends in Compressive Sensing for EEG Signal Processing Applications

Gurve

Delisle-Rodríguez

Bastos

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…To improve the performance of the compressive sensing approach for EEG signal Fourier transform and the non-convex optimizationbased algorithm is used. Normalized Mean square error is calculated for the recovered EEG signal [1]. Another work in Mohammad Salim is with Malaviya National Institute of Technology, India (e-mail: 2018rec9028@mnit.ac.in, msalim.ece@mnit.ac.in).…”

Section: Compressive Sensing Framework For Eeg Signalmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Upadhyaya

Salim

2020

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Compressive Sensing of Multichannel EEG Signals Based on Graph Fourier Transform and Cosparsity

Cited by 8 publications

References 22 publications

Trends in Compressive Sensing for EEG Signal Processing Applications

Trends in Compressive Sensing for EEG Signal Processing Applications

Trends in Compressive Sensing for EEG Signal Processing Applications

International Journal of Electronics and Telecommunications

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