Complex Blind Source Extraction From Noisy Mixtures Using Second-Order Statistics

Javidi, Soroush; Mandic, Danilo P.; Cichocki, Andrzej

doi:10.1109/tcsi.2010.2043985

Cited by 45 publications

(31 citation statements)

References 32 publications

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“…The blind source separation (BSS) problem [28], [29] deserves to be solved in the EEG signal which is usually con- taminated by various artifacts including eye movement and indoor power-line noise [30], [31]. One of the popular methods was applied ICA to find the linear projections that maximizes the mutual independences of estimated components.…”

Section: A Icamentioning

confidence: 99%

Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System

Lin

Chuang

et al. 2012

IEEE Trans. Circuits Syst. I

129

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A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks ( -value 0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.

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Section: A Icamentioning

confidence: 99%

Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System

Lin

Chuang

et al. 2012

IEEE Trans. Circuits Syst. I

129

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“…One the most popular categories of methods is based on blind source separation (BSS) [12,13]. It has been proved that ICA is an effective and generally applicable method for treating artifacts, including OA separation [14,15].…”

Section: Introductionmentioning

confidence: 99%

Automatic and Direct Identification of Blink Components from Scalp EEG

Kong

Zhou

et al. 2013

Sensors

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Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn't need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects.

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“…A quaternion widely linear predictor is used to extract both Q-proper and Q-improper sources, based on the smallest normalised prediction error, making such BSE independent of source powers. This is a generalisation of the complex widely linear prediction based BSE algorithm in [10], and is supported by simulations on both Q-proper and Q-improper signals, coming from synthetic and real-world scenarios.…”

Section: Introductionmentioning

confidence: 78%

Blind extraction of improper quaternion sources

Javidi

Took

Jahanchahi

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Blind extraction of quaternion-valued latent sources is addressed based on their local temporal properties. The extraction criterion is based on the minimum mean square widely linear prediction error, thus allowing for the extraction of both proper and improper quaternion sources. The use of the widely linear adaptive predictor is justified by the relationship between the mean square prediction error and the crosscorrelation and cross-pseudocorrelations of the source signals. Simulations on benchmark improper quaternion sources together with a real-world example of EEG artifact removal illustrate the usefulness of the proposed methodology. © 2011 IEEE

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Complex Blind Source Extraction From Noisy Mixtures Using Second-Order Statistics

Cited by 45 publications

References 32 publications

Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System

Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System

Automatic and Direct Identification of Blink Components from Scalp EEG

Blind extraction of improper quaternion sources

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