Applying Outlier Detection and Independent Component Analysis for Compressed Sensing EEG Measurement Framework

Katsumata, Shun; Kanemoto, Daisuke; Ohki, Makoto

doi:10.1109/biocas.2019.8919117

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…When CR = 4 or more, there are slight differences in the reconstruction accuracy; however, it is clear that the proposed framework can recover with a higher accuracy than only CS, although the proposed framework does not need MPUs to operate ICA in the sensing unit. If a higher reconstruction accuracy is desired, then an additional signal processing can be accepted in the data processing unit; for example, applying an additional method for removing artifacts efficiently [25] is also one solution.…”

Section: Discussionmentioning

confidence: 99%

Compressed Sensing Framework Applying Independent Component Analysis after Undersampling for Reconstructing Electroencephalogram Signals

Kanemoto

Katsumata

Aihara

et al. 2020

IEICE Trans. Fundamentals

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This paper proposes a novel compressed sensing (CS) framework for reconstructing electroencephalogram (EEG) signals. A feature of this framework is the application of independent component analysis (ICA) to remove the interference from artifacts after undersampling in a data processing unit. Therefore, we can remove the ICA processing block from the sensing unit. In this framework, we used a random undersampling measurement matrix to suppress the Gaussian. The developed framework, in which the discrete cosine transform basis and orthogonal matching pursuit were used, was evaluated using raw EEG signals with a pseudo-model of an eye-blink artifact. The normalized mean square error (NMSE) and correlation coefficient (CC), obtained as the average of 2,000 results, were compared to quantitatively demonstrate the effectiveness of the proposed framework. The evaluation results of the NMSE and CC showed that the proposed framework could remove the interference from the artifacts under a high compression ratio.

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Section: Discussionmentioning

confidence: 99%

Compressed Sensing Framework Applying Independent Component Analysis after Undersampling for Reconstructing Electroencephalogram Signals

Kanemoto

Katsumata

Aihara

et al. 2020

IEICE Trans. Fundamentals

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“…There are other recent studies that have discussed this issue (see, e.g., [25][26][27]), where the ICA is performed after the CS. In fact, there are major drawbacks of this method due to the neglect of two important factors: 1) The use of a Gaussian random measurement matrix Φ ∈ R m×n in the sensing unit to measure sparse biosignals usually following non-Gaussian distributions (e.g., sub-Gaussian, super-Gaussian, or mixed super-, sub-, and Gaussian distributions) [28].…”

Section: Icamentioning

confidence: 99%

An Information-Theoretic Framework for Joint CS-ICA Recovery of Sparse Biosignals

Alghorani¹,

Ikki²

2023

Preprint

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<div>The aim of this study is to propose an information-theoretic framework for compressed sensing (CS)-independent</div><div>component analysis (ICA) algorithms that can be used for the joint recovery of sparse biosignals. The proposed framework supports real-time patient monitoring systems that enhance the detection, tracking, and monitoring of vital signs remotely via wearable biosensors. Specifically, we address the problem of sparse signal recovery and acquisition in wearable biosensor networks, where we</div><div>present a new analysis of CS-ICA algorithms from an information theory perspective to compute the sampling rate required to recover sparse biosignals corrupted by motion artifacts and interference, which to the best of our knowledge, has not been studied before. Our analysis and examples indicate that the proposed approach helps to develop low-cost, low-power edge computing devices while</div><div>improving data quality and accuracy for a given measurement. We also show that, under noisy measurement conditions, the CS-ICA algorithm can outperform the standard CS method, where a biosignal can be retrieved in only a few measurements. By implementing</div><div>the sensing framework, the error in reconstructing biosignals is reduced, and a digital-to-analog converter operates at low-speed and low-resolution.</div>

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“…We created a dictionary matrix using the K-SVD dictionary learning algorithm with EEG signals based on the CHB-MIT scalp EEG database [12]. The sampling rate of the EEG signals was changed from 256 Hz/sample to 200 Hz/sample to be the same as that used in [6], [7], [9] When using the K-SVD dictionary for a CS framework with OD-ICA, it is necessary to reveal the optimal sparse parameters during training and reconstruction, as well as the suitable number of the dictionary matrix size. Considering the relationship with the sparse parameter r when using the OMP algorithm in the reconstruction, we determined the optimal s parameter.…”

Section: Dictionary Creationmentioning

confidence: 99%

“…For comparison purposes, the region containing pseudoeye-blink artifact was not considered when calculating the NMSE to realize same evaluation as [6], [7], and [9]. At first, suitable sparse parameters are evaluated.…”

Section: Reconstruction Of Compressed Eegsmentioning

confidence: 99%

“…Thus, remove eyeblink artifacts before reconstruction is desirable [6], [7]. Recently, a framework was proposed to efficiently remove the eye-blink artifacts before reconstruction by applying outlier detection and independent component analysis (OD-ICA) [8] to compressed EEG signals [9]. However, increasing the reconstruction accuracy is still a critical aspect because further improvements in reconstruction accuracy will allow CS to be used in also several other applications.…”

Section: Introductionmentioning

confidence: 99%

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Applying K-SVD Dictionary Learning for EEG Compressed Sensing Framework with Outlier Detection and Independent Component Analysis

Nagai

Kanemoto

Ohki

2021

IEICE Trans. Fundamentals

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This letter reports on the effectiveness of applying the Ksingular value decomposition (SVD) dictionary learning to the electroencephalogram (EEG) compressed sensing framework with outlier detection and independent component analysis. Using the K-SVD dictionary matrix with our design parameter optimization, for example, at compression ratio of four, we improved the normalized mean square error value by 31.4% compared with that of the discrete cosine transform dictionary for CHB-MIT Scalp EEG Database.

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Applying Outlier Detection and Independent Component Analysis for Compressed Sensing EEG Measurement Framework

Cited by 16 publications

References 9 publications

Compressed Sensing Framework Applying Independent Component Analysis after Undersampling for Reconstructing Electroencephalogram Signals

Compressed Sensing Framework Applying Independent Component Analysis after Undersampling for Reconstructing Electroencephalogram Signals

An Information-Theoretic Framework for Joint CS-ICA Recovery of Sparse Biosignals

Applying K-SVD Dictionary Learning for EEG Compressed Sensing Framework with Outlier Detection and Independent Component Analysis

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