Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis

Castellanos, Nazareth P.; Makarov, Valeri A.

doi:10.1016/j.jneumeth.2006.05.033

Cited by 506 publications

(356 citation statements)

References 47 publications

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“…However, one of the drawbacks with ICA is that it needs visual inspection to extract artificial components. Therefore, Wavelet denoising has been combined with ICA to suppress artifacts from the EEG signals [69][70][71][72].…”

Section: Methods and Approachesmentioning

confidence: 99%

In-Vehicle Stress Monitoring Based on EEG Signal

Begum¹,

Ahmed²

2017

IJERA

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In recent years, improved road safety by monitoring human factors i.e., stress, mental load, sleepiness, fatigue etc. of vehicle drivers has been addressed in a number of studies. Due to the individual variations and complex dynamic in-vehicle environment systems that can monitor such factors of a driver while driving is challenging. This paper presents a drivers' stress monitoring system based on electroencephalography (EEG) signals enabling individual---focused computational approach that can generate automatic decision. Here, a combination of different signal processing i.e., discrete wavelet transform, largest Lyapunov exponent (LLE) and modified covariance have been applied to extract key features from the EEG signals. Hybrid classification approach Fuzzy-CBR (case-based reasoning) is used for decision support. The study has focused on both long and short-term temporal assessment of EEG signals enabling monitoring in different time intervals. In short time interval, which requires complex computations, the classification accuracy using the proposed approach is 79% compare to a human expert. Accuracy of EEG in developing such system is also compared with other reference signals e.g., Electrocardiography (ECG), Finger temperature, Skin conductance, and Respiration. The results show that in decision making the system can handle individual variations and provides decision in each minute time interval.

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Section: Methods and Approachesmentioning

confidence: 99%

In-Vehicle Stress Monitoring Based on EEG Signal

Begum¹,

Ahmed²

2017

IJERA

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“…1), the raw EEG is further processed by a state-of-the-art AAR algorithm. Motived by our recent findings [27], we used the wavelet-enhanced independent component analysis (wICA) algorithm [28].…”

Section: B Eeg Acquisition Pre-processing and Aarmentioning

confidence: 99%

“…The wICA method is summarized in five steps: (1) the EEG data is decomposed into independent components (IC); (2) the wavelet transform is applied to the ICs; (3) thresholding of the wavelet coefficients is performed to differentiate between neural and artefactual coefficients; (4) the inverse wavelet transform is applied to the thresholded coefficients, retrieving ICs with only neural activity; and lastly, (5) wavelet-corrected ICs are projected to obtain the artefact-free EEG data. The wICA algorithm provides an improved performance and better preservation of EEG spectral and phase coherence properties compared with ICA algorithm [28], [29]. For illustration purposes, Figure 3 depicts a representative EEG segment before (blue) and after wICA processing (red).…”

Section: B Eeg Acquisition Pre-processing and Aarmentioning

confidence: 99%

Towards automated EEG-Based Alzheimer's disease diagnosis using relevance vector machines

Cassani

Falk

Fraga

et al. 2014

5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC)

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Abstract-Existing electroencephalography (EEG) basedAlzheimer's disease (AD) diagnostic systems typically rely on experts to visually inspect and segment the collected signals into artefact-free epochs and on support vector machine (SVM) based classifiers. The manual selection process, however, introduces biases and errors into the diagnostic procedure, renders it "semiautomated," and makes the procedure costly and labour-intensive. In this paper, we overcome these limitations by proposing the use of an automated artefact removal (AAR) algorithm to remove artefacts from the EEG signal without the need for human intervention. We investigate the effects of the so-called waveletenhanced independent component analysis (wICA) AAR on three classes of EEG features, namely spectral power, coherence, and amplitude modulation, and ultimately, on diagnostic accuracy, specificity and sensitivity. Furthermore, we propose to replace the binary SVM classifier with a soft-decision relevance vector machine (RVM) classifier. Experimental results show the proposed RVM-based system outperforming the SVM trained on features extracted from both manually-selected and wICAprocessed epochs. Moreover, the class membership information output by the RVM is shown to provide clinicians with a richer pool of information to assist with AD assessment.

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“…This research attempts to combine the advantages of independent component analysis with the capabilities of certain dynamic models to deal with the temporal variability of the EEG. For instance, some representative examples are the study of developmental differences in the saccadic contingent negative variation [138], EEG and event-related potential (ERP) data [162,277], and removal of artifacts in the EEG signal [44]. The GTS has already published some works in this area [230].…”

Section: Application On Electroencephalographic Signalsmentioning

confidence: 99%

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

Armero¹

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This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability densities, greatly simplifying the modeling of the data. ICA mixture models (ICAMM) provide further flexibility by alleviating the independency requirement of ICA, thus allowing the model to obtain local projections of the data without compromising its generalization capabilities. Here are explored new possibilities of ICAMM for the purposes of estimation and classification of signals.The thesis makes several contributions to the research in non-Gaussian mixtures: (i) a method for maximum-likelihood estimation of missing data, based on the maximization of the PDF of the data given the ICAMM; (ii) a method for Bayesian estimation of missing data that minimizes the mean squared error and can obtain the confidence interval of the prediction; (iii) a generalization of the sequential dependence model for ICAMM to semi-supervised or supervised learning and multiple chains of dependence, thus allowing the use of multimodal data; and (iv) introduction of ICAMM in diverse novel applications, both for estimation and for classification.The developed methods were validated via an extensive number of simulations that covered multiple scenarios. These tested the sensitivity of the proposed methods with respect to the following parameters: number of values to estimate; kinds of source distributions; correspondence of the data with respect to the assumptions of the model; number of classes in the mixture model; and unsupervised, semi-supervised, and supervised learning. The performance of the proposed methods was evaluated using several figures of merit, and compared with the performance of multiple classical and state-of-the-art techniques for estimation and classification.Aside from the simulations, the methods were also tested on several sets of real data from different types: data from seismic exploration studies; ground penetrating radar surveys; and biomedical data. These data correspond to the following applications: reconstruction of damaged or missing data from ground-penetrating radar surveys of historical walls; reconstruction of damaged or missing data from a seismic exploration survey; reconstruction of artifacted or missing electroencephalographic (EEG) data; diagnosis of sleep disorders; modeling of the brain response during memory tasks; and exploration of EEG data from subjects performing a battery of neuropsychological tests. The obtained results demonstrate the capability of the proposed methods to work on problems wi...

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Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis

Cited by 506 publications

References 47 publications

In-Vehicle Stress Monitoring Based on EEG Signal

In-Vehicle Stress Monitoring Based on EEG Signal

Towards automated EEG-Based Alzheimer's disease diagnosis using relevance vector machines

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

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