Comparison of Inter-and Intra-Subject Variability of P300 Spelling Dictionary in EEG Compressed Sensing

Fira, Monica; Goraș, Liviu

doi:10.14569/ijacsa.2016.071049

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…There has been a variety of sensing matrices proposed in the literature for EEG signal samplings. Such as random matrices [55] [33][50] [52], structural matrices, and deterministic matrices [56] [57]. Several random matrices have been proposed in the literature for sampling EEG signals, such as Random Gaussian Matrices (RGM) with entries representing absolute values, Bernoulli matrices with entries representing 1's, and random binary matrices (RBMs) containing 0's and 1's.…”

Section: ) Sensing Matricesmentioning

confidence: 99%

“…F. C. Morabito et al [51] used the same approach with the Gabor dictionary to reconstruct the signal with a high compression ration and detect Alzheimer's disease (AD), Mild Cognitive Impaired (MCI), and Healthy Control (HC) diseases. Other authors [52] [33] also used the 𝑙 1 − 𝑛𝑜𝑟𝑚 for P300 detection spelling. Furthermore, authors in [61][50] [49] used this approach to compare the performance with other algorithms.…”

Section: ) Sensing Matricesmentioning

confidence: 99%

“…Choosing the appropriate sparse dictionary or bases matrix is critically important because signal reconstruction performance depends on the input signals ' ). However, few studies examined the data-driven dictionaries, such as authors of [52] comparing the results obtained from the Daubechies 10 wavelet dictionary and temporal and channel-specific EEG signal dictionaries. They utilized a dataset obtained from the BCI-Competition III 2005-P300 Spelling, and Normalized Percent of Root-Mean-Square difference (PRDN), Normalized Mean Square Error (NMSE), RMS (root mean square error), and PRD (percentage root-mean-square difference) are evaluated for given dictionaries.…”

Section: ) Sparse Basesmentioning

confidence: 99%

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Compressed Sensing Approach for Physiological Signals: A Review

et al. 2023

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The immense progress in physiological signal acquisition and processing in health monitoring allowed a better understanding of patient disease detection and diagnosis. With the increase in data volume and power consumption, effective data compression, signal acquisition, transmission, and processing techniques are essential, especially in telemonitoring healthcare applications. An emerging research area focuses on integrating Compressed Sensing (CS) with physiological signals to deal with a massive amount of physiological data, transmission bandwidth, and power-saving purposes. A review of compressed sensing for physiological signals is presented in this paper, including electroencephalography (EEG), electrocardiography (EEG), electromyography (EMG), and electrodermal activity (EDA), focusing on the pros and cons of CS in treating such signals and the suitability of CS for hardware implementation. Furthermore, we emphasize performance matrices such as Compression Ratio (CR), Signal to-Noise-Ratio (SNR), Percentage Root-mean-square Difference (PRD), and Processing time to evaluate the performance of CS. We also investigate the current practices, challenges, and opportunities of using CS in healthcare applications.

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Section: ) Sensing Matricesmentioning

confidence: 99%

Section: ) Sensing Matricesmentioning

confidence: 99%

Section: ) Sparse Basesmentioning

confidence: 99%

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Compressed Sensing Approach for Physiological Signals: A Review

et al. 2023

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“…Similarly, M. Fira et al [54] used l 1 -norm for EEG signal reconstruction for the analysis of the specific dictionaries of EEG signals. Furthermore, M. Fira et al [77] also used l 1 -norm in their study and proposed CS based framework for the P300 detection spelling paradigm.…”

Section: Reconstruction Algorithmsmentioning

confidence: 99%

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.

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“…Similarly, M. Fira et al [ 54 ] used

-norm for EEG signal reconstruction for the analysis of the specific dictionaries of EEG signals. Furthermore, M. Fira et al [ 77 ] also used

-norm in their study and proposed CS based framework for the P300 detection spelling paradigm.…”

Section: Cs For Eeg Signalmentioning

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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Comparison of Inter-and Intra-Subject Variability of P300 Spelling Dictionary in EEG Compressed Sensing

Abstract: Abstract-In this paper, we propose a new compression method for electroencephalographic signals based on the concept of compressed sensing (CS) for the P300 detection spelling paradigm. The method uses a universal mega-dictionary which has been found not to be patient-specific.

Cited by 8 publications

References 18 publications

Compressed Sensing Approach for Physiological Signals: A Review

Compressed Sensing Approach for Physiological Signals: A Review

Trends in Compressive Sensing for EEG Signal Processing Applications

Trends in Compressive Sensing for EEG Signal Processing Applications

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