A Novel Underdetermined Source Recovery Algorithm Based on k-Sparse Component Analysis

Eqlimi, Ehsan; Makkiabadi, Bahador; Samadzadehaghdam, Nasser; Khajehpour, Hassan; Mohagheghian, Fahimeh; Sanei, Saeid

doi:10.1007/s00034-018-0910-9

Cited by 13 publications

(10 citation statements)

References 54 publications

(73 reference statements)

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“…Although defining microstates using GFP peaks partially addresses this issue, it still cannot guarantee single microstate activity, even at peak points. To tackle situations where multiple microstates are active simultaneously, in addition to our proposed method, certain algorithms, such as k ‐sparse component analysis ( k ‐SCA), may be employed (Eqlimi et al, 2015, 2019, 2020).…”

Section: Methodsmentioning

confidence: 99%

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Eqlimi,

Bockstael,

Schönwiesner

et al. 2023

Eur J of Neuroscience

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Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two‐stage time course for both microstate complexity and alpha‐to‐theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.

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Section: Methodsmentioning

confidence: 99%

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Eqlimi,

Bockstael,

Schönwiesner

et al. 2023

Eur J of Neuroscience

Self Cite

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“…Sparsity-based practices focus on extracting the non-negative sources. Much work has been carried out on what is called SCA [ 14 , 15 , 16 , 17 , 18 ]. Several techniques for obtaining sparsity in the transform domain have been developed thus far, including the short-time Fourier Transform (STFT) and the wavelet packet transform [ 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)

Hassan

Ramli

2023

Sensors

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Blind source separation (BSS) recovers source signals from observations without knowing the mixing process or source signals. Underdetermined blind source separation (UBSS) occurs when there are fewer mixes than source signals. Sparse component analysis (SCA) is a general UBSS solution that benefits from sparse source signals which consists of (1) mixing matrix estimation and (2) source recovery estimation. The first stage of SCA is crucial, as it will have an impact on the recovery of the source. Single-source points (SSPs) were detected and clustered during the process of mixing matrix estimation. Adaptive time–frequency thresholding (ATFT) was introduced to increase the accuracy of the mixing matrix estimations. ATFT only used significant TF coefficients to detect the SSPs. After identifying the SSPs, hierarchical clustering approximates the mixing matrix. The second stage of SCA estimated the source recovery using least squares methods. The mixing matrix and source recovery estimations were evaluated using the error rate and mean squared error (MSE) metrics. The experimental results on four bioacoustics signals using ATFT demonstrated that the proposed technique outperformed the baseline method, Zhen’s method, and three state-of-the-art methods over a wide range of signal-to-noise ratio (SNR) ranges while consuming less time.

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“…Sparse component analysis, as a common UBSS method, can separate the source signals by exploiting the sparsity characteristics of sources in the transform domain [ 21 , 22 ]. Generally, the SCA algorithm consists of two steps: mixing matrix estimation and source recovery [ 23 ]. Among the two-step approach, the mixing matrix estimation has been widely considered to be the most important step [ 15 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction for Reciprocating Compressors Based on Underdetermined Blind Source Separation

Wang

Chen

Zhao

et al. 2021

Entropy

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In practical engineering applications, the vibration signals collected by sensors often contain outliers, resulting in the separation accuracy of source signals from the observed signals being seriously affected. The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. Therefore, a two-stage clustering method is proposed by combining hierarchical clustering and K-means to improve the reliability of the estimated mixing matrix in this paper. The proposed method is used to solve the two major problems in the K-means algorithm: the random selection of initial cluster centers and the sensitivity of the algorithm to outliers. Firstly, the observed signals are clustered by hierarchical clustering to get the cluster centers. Secondly, the cosine distance is used to eliminate the outliers deviating from cluster centers. Then, the initial cluster centers are obtained by calculating the mean value of each remaining cluster. Finally, the mixing matrix is estimated with the improved K-means, and the sources are recovered using the least square method. Simulation and the reciprocating compressor fault experiments demonstrate the effectiveness of the proposed method.

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A Novel Underdetermined Source Recovery Algorithm Based on k-Sparse Component Analysis

Cited by 13 publications

References 54 publications

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)

Fault Feature Extraction for Reciprocating Compressors Based on Underdetermined Blind Source Separation

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