scholarly journals Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Zeng ◽  
Aiguo Song
Keyword(s):  
Covariance Matrix ◽  
Blind Source Separation ◽  
Source Separation ◽  
Experimental Results ◽  
Eeg Signals ◽  
Subspace Analysis ◽  
Eeg Data ◽  
The Mean ◽  
Eeg Recordings ◽  
Ocular Artifacts

An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA) algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.

The Relationship between Frequency Domain Blind Source Separation and Frequency Domain Adaptive Beamformer

2014 ◽  
Vol 490-491 ◽  
pp. 654-662
Author(s):  
Si Chong Qian ◽  
Yang Xiang
Keyword(s):  
Frequency Domain ◽  
Mean Square Error ◽  
Blind Source Separation ◽  
Array Signal Processing ◽  
Source Separation ◽  
Adaptive Beamforming ◽  
Separation Performance ◽  
Mean Square ◽  
The Mean ◽  
The Relationship

As two important methods of array signal processing, blind source separation and beamforming can extract the target signal and suppress interference by using the received information of the array element. In the case of convolution mixture of sources, frequency domain blind source separation and frequency domain adaptive beamforming have similar signal model. To find the relationship between them, comparison between the minimization of the off-diagonal components in the BSS update equation and the minimization of the mean square error in the ABF had been made from the perspective of mathematical expressions, and find that the unmixing matrix of the BSS and the filter coefficients of the ABF converge to the same solution in the mean square error sense under the condition that the two source signals are ideally independent. With MATLAB, the equivalence in the frequency domain have been verified and the causes affecting separation performance have been analyzed, which was achieved by simulating instantaneous and convolution mixtures and separating mixture speech in frequency-domain blind source separation and frequency domain adaptive beamforming way.

scholarly journals Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yu Qi ◽  
Yueming Wang ◽  
Jianmin Zhang ◽  
Junming Zhu ◽  
Xiaoxiang Zheng
Keyword(s):  
Seizure Detection ◽  
Simultaneous Optimization ◽  
Eeg Signals ◽  
Deep Network ◽  
Scalp Eeg ◽  
Impulsive Noises ◽  
Eeg Data ◽  
The Mean ◽  
Maximum Correntropy Criterion

Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE) as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC) is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE), the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

scholarly journals PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Kenneth Ball ◽  
Nima Bigdely-Shamlo ◽  
Tim Mullen ◽  
Kay Robbins
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Simulated Data ◽  
Real Data ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Data ◽  
Original Observation ◽  
Observation Space

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we callPairwise Complex Independent Component Analysis(PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals.

Blind-source separation of seismic signals based on information maximization

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. V119-V130 ◽  
Author(s):  
Kuang-Hung Liu ◽  
William H. Dragoset
Keyword(s):  
Blind Source Separation ◽  
Mean Squared Error ◽  
Source Separation ◽  
Seismic Signals ◽  
Subtraction Method ◽  
Squared Error ◽  
Information Maximization ◽  
The Mean ◽  
Maximization Principle

Prediction methods for seismic multiples are never ideal in practice and an adaptive subtraction process is needed to account for mismatches between the predicted and the actual multiples. We are interested in the problem of separating primary and multiple seismic signals based on their statistical properties. We link recent advances in the blind-source separation problem to the multiple removal problem, and present a novel adaptive subtraction method based on an information maximization principle. Compared with previous methods, our proposed method uses higher-order statistics of the data and incorporates the filtering nature of the adaptive subtraction problem into our algorithm formulation. We use simulations to show that our proposed adaptive subtraction method outperforms the popular least-squares adaptive subtraction and the independent component analysis methods quantitatively, as measured by the mean-squared error, and qualitatively, as evaluated by the visual quality of the image reconstruction.

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