An Efficient Jacobi-Like Deflationary ICA Algorithm: Application to EEG Denoising

Sardouie, Sepideh Hajipour; Albera, Laurent; Shamsollahi, Mohammad Bagher; Merlet, Isabelle

doi:10.1109/lsp.2014.2385868

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…Independent component analysis is a multi-dimensional probabilistic analysis method for searching a linear/nonlinear transform to recover components that are maximally independent to each other [52], [53]. The commonly used linear mixing model of ICA is…”

Section: Preliminarymentioning

confidence: 99%

Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications

Zhang

Zheng

et al. 2019

IEEE Access

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It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way. INDEX TERMS Yawn, blink, blood volume pulse (BVP), drowsiness detection, second-order blind identification (SOBI).

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Section: Preliminarymentioning

confidence: 99%

Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications

Zhang

Zheng

et al. 2019

IEEE Access

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“…Initially, it was developed for the solution of blind signal separation. Recently, ICA has become a powerful tool for signal processing and data analysis [28,29].…”

Section: Data De-noising Processing By Fast Independent Component Anamentioning

confidence: 99%

Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search

Niu

Zhang

et al. 2017

Energies

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Abstract:With the increase in energy demand, extreme climates have gained increasing attention. Ice disasters on transmission lines can cause gap discharge and icing flashover electrical failures, which can lead to mechanical failure of the tower, conductor, and insulators, causing significant harm to people's daily life and work. To address this challenge, an intelligent combinational model is proposed based on improved empirical mode decomposition and support vector machine for short-term forecasting of ice cover thickness. Firstly, in light of the characteristics of ice cover thickness data, fast independent component analysis (FICA) is implemented to smooth the abnormal situation on the curve trend of the original data for prediction. Secondly, ensemble empirical mode decomposition (EEMD) decomposes data after denoising it into different components from high frequency to low frequency, and support vector machine (SVM) is introduced to predict the sequence of different components. Then, some modifications are performed on the standard SVM algorithm to accelerate the convergence speed. Combined with the advantages of genetic algorithm and tabu search, the combination algorithm is introduced to optimize the parameters of support vector machine. To improve the prediction accuracy, the kernel function of the support vector machine is adaptively adopted according to the complexity of different sequences. Finally, prediction results for each component series are added to obtain the overall ice cover thickness. A 220 kV DC transmission line in the Hunan Region is taken as the case study to verify the practicability and effectiveness of the proposed method. Meanwhile, we select SVM optimized by genetic algorithm (GA-SVM) and traditional SVM algorithm for comparison, and use the error function of mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) to compare prediction accuracy. Finally, we find that these improvements facilitate the forecasting efficiency and improve the performance of the model. As a result, the proposed model obtains more ideal solutions and has higher accuracy and stronger generalization than other algorithms.

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“…S.H. Sardouie et al [33] worked on the elimination of muscle artifact from the EEG in the paediatric patient. In this technique JDICA is used to eliminatethe artifact.…”

Section: Reviewmentioning

confidence: 99%

A Review on EEG Artifacts And Its Different Removal Technique

Jung¹

2016

APJCRI

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Electroencephalograms are the neurological signals which help in the study of various diseases. These are often contaminated with various artifacts. It is difficult to study and analysis of brain signals in the existence of these artifacts. EOG, ECG, motion and EMG are the common artifacts which cause disturbance to neurological signal. This review paper focuses on the artifact removal techniques with their features. Important parameters were taken into consideration while the study of various published papers. Strength and weakness of each paper are mentioned. This review of various papers is best of my knowledge.

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An Efficient Jacobi-Like Deflationary ICA Algorithm: Application to EEG Denoising

Cited by 17 publications

References 10 publications

Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications

Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications

Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search

A Review on EEG Artifacts And Its Different Removal Technique

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