Enhancement of Source Separation Based on Efficient Stone's BSS Algorithm

Abdullah, Ali A.; Zhang, Chao Zhu

doi:10.14257/ijsip.2014.7.2.39

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Stone BSS technique exploits temporal predictability property to separate the mixed signals unlike other BSS technique that use different properties to implement separation (Abdullah and Zhu, 2014). Stone estimation depends on very simple principle which is that the temporal predictability must be equal or less than its components and this step helps to select every single weight for each vector to obtain orthogonal projection (Stone, 2002).…”

Section: Stone Blind Source Separationmentioning

confidence: 99%

Twin Fetus Ecg Signal Extraction Based on Temporal Predictability

Talib

Abdullah

Hamzah³

2021

Kufa J. Eng.

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The most popular defects that infect new born babies have close connection with heart. Approximately 1% percent of new born babies suffering from defects that are caused by heart. By observing Electrocardiogram "ECG" during gestation doctors can study the fetal heart activity that collected from mother abdominal and rectify number of defects which has been diagnosed in fetal heart. Many techniques like filters, BSS and even artificial intelligence used to extract and process fetal ECG signal. In case of twins gestation the problem would be more complicated because the recorded signal is a mixture of multi signals which are mother ECG, fetuses ECG and noises where every signal comes from different source in addition fetus have the same ECG signal features as well. In this study the concentration would be on BSS techniques specifically on Stone BSS method. A comparison has been made between Stone method and two other methods "EFICA and JADE". The results proved that Stone method has better performance comparing to the other BSS techniques

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Section: Stone Blind Source Separationmentioning

confidence: 99%

Twin Fetus Ecg Signal Extraction Based on Temporal Predictability

Talib

Abdullah

Hamzah³

2021

Kufa J. Eng.

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“…According to Ahmed K. et al [12], the generation of artificial EEG signals is polluted with EOG and power line noise using classical event-related potentials (ERP) theory. The additional signal and noise components produce the simulated data.…”

Section: Simulated Eeg Data Setmentioning

confidence: 99%

“…The Stone BSS among the source separation methods, that with no details on the source signals or the mixed matrix, it can distinguish the source signals from the mixed signals. Ahmed K. et al [12] proposed a modification of Stone's BSS based on the Fast Genetic Algorithm (FGA), and hybridization was used to generate and modify the optimal half-life (hL, hS) parameters that affect the stone system separation mechanism by using the reactions of two separate linear scalar filters for the same set of signals. Fast genetic algorithms are an effective method to improve the process of segregation when hybridised with SBSS algorithms.…”

Section: Introductionmentioning

confidence: 99%

Effective Hybrid Method for the Detection and Rejection of Electrooculogram (EOG) and Power Line Noise Artefacts From Electroencephalogram (EEG) Mixtures

Ahmed

AlShemmary

2020

IEEE Access

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Electrooculogram (EOG) and power line noise artefact detection and rejection have commonly utilized Stone's blind source separation (Stone's BSS) algorithm. The paper suggests a hybrid method between particle swarm optimization (PSO) and Stone's BSS for the detection and rejection of electrooculogram (EOG) and power line noise in the single-channel without the use of a notch filter. The proposed method contains three major steps: centralizing and whitening of the input EEG signal, incorporating the processing EEG signal into the iterative algorithm of the particle swarm optimization (PSO) to randomly generate the optimal value of (hshort, hlong) and weight vector W parameters, and applying Stone's BSS using the generalized eigenvalue decomposition (GEVD) method to eliminate electrooculogram (EOG) and power line noise artefacts to obtain a clean EEG signal. The authors assess the robustness of the suggested method evaluated using real and simulated electroencephalogram (EEG) data sets. The simulated electroencephalogram (EEG) data and electrooculogram (EOG) and line noise (LN) artefacts are produced and mixed randomly in the MATLAB program; two types of real EEG data are taken in 9 and 19 channels. Evaluation results show the proposed algorithms as effective techniques for extracting both the power line noise and electrooculogram (EOG) artefacts from brain mixtures compared to specific BSS algorithms (e.g., Stone's BSS, evolutionary fast independent component analysis (EFICA), fast independent component analysis (FastICA), and joint approximate diagonalization of Eigen matrices (JADE)) while preserving the clinical features of the reconstructed EEG signal.

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“…The disadvantage of this method is that the eye blinking cannot be removed completely, but just reduced. Ahmed Karem [4] used evolutionary SBSS to delete artefacts such as ballistocardiogram BCG, electro-oculogram EOG from EEG signals measured inside BCI. The proposed solution overcomes the problems associated with the conventional method, such as source opacity, disordered independent components and a large number of independent components.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

Ahmed

AlShemmary

2020

IEEE Access

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Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (ℎlong, ℎshort) and weight vector W parameters, which affect the convergence of the algorithm. This paper proposes a hybridization of Stone's BSS with Particle Swarm Optimization (PSO) to boost the separation process. An improved Stone's BSS (ISBSS) method is employed to reject eye blinking from the electroencephalogram (EEG) mixture. The electroencephalogram (EEG) mixed-signal is first centralized and whitened; then, it is incorporated into the particle swarm optimization (PSO) iterative algorithm to process the initial (ℎlong, ℎshort) and generate the weight vector W parameters randomly. Finally, the generalized eigenvalue decomposition (GEVD) method is applied to extract EEG singles to obtain a clean EEG signal. A clinical EEG database is used to test the improved and other algorithms. The GEVD method estimates the measurement performance of the proposed algorithm using a carrier-to-interference ratio and integral square error and compares the proposed algorithm with the conventional Stone's BSS, fast independent component analysis (FastICA), evolutionary fast independent component analysis (EFICA), and joint approximate diagonalization of eigen matrices (JADE) algorithms to check its effectiveness. The results show that the suggested hybrid method has a better performance and decreasing elapsed time than conventional Stone's BSS and other algorithms.

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Enhancement of Source Separation Based on Efficient Stone's BSS Algorithm

Abstract: An efficient Stone's BSS (ESBSS) algorithm is proposed based on the joint between original Stone's BSS (SBSS) and genetic algorithm (GA

Cited by 5 publications

References 18 publications

Twin Fetus Ecg Signal Extraction Based on Temporal Predictability

Twin Fetus Ecg Signal Extraction Based on Temporal Predictability

Effective Hybrid Method for the Detection and Rejection of Electrooculogram (EOG) and Power Line Noise Artefacts From Electroencephalogram (EEG) Mixtures

Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

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