Journal ArticleDOI
Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality With Reference to Noise-Free EEG Signal
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TLDR
Two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band of EEG signal are proposed.Abstract:
An objective distortion measure is very crucial to accurately quantify the distortion introduced in the electroencephalogram (EEG) signal during the denoising process Most of the existing algorithms report their denoising performance by comparing the original EEG signal and the reconstructed EEG signal using root mean square error (RMSE) and other similar measures However, it is very important to quantify the distortion in each band of EEG signal since each band provides distinct information about the specific brain activity Furthermore, quantification of band-wise distortion enables the selection of particular denoising algorithm for the application-specific EEG signal analysis Therefore, in this paper, we propose two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band These performance measures are computed between the wavelet subbands of the original and the denoised/reconstructed signal with weights equal to the relative wavelet energy and wavelet entropy of the corresponding subband wavelet coefficients To demonstrate the effectiveness of the proposed performance measures, we evaluate the performance of six existing denoising methods using these measures Results depict that these measures can adequately provide high mutual agreement between objective scores and subjective analysisread more
Citations
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Automated Ocular Artifacts Removal Framework Based on Adaptive Chirp Mode Decomposition
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TL;DR: Comparison performance analysis demonstrates that the proposed framework outperforms the existing OAs removal techniques based on wavelet thresholding, variational mode decomposition (VMD), and Savitzky-Golay filter (SG-filter).
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Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
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Cardiac Artifact Noise Removal From Sleep EEG Signals Using Hybrid Denoising Model
TL;DR: A hybrid signal denoising methodology, which includes empirical wavelet transforms (EWTs), adaptive threshold-based nonlinear Teager–Kaiser energy operator (TEO), and customized morphological filter in accompanying with modified ensemble average subtraction (MEAS) is proposed for automatic detection and suppression of cardiac artifact from a single-channel EEG.
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Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface.
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Research on the Identification Method of Audiovisual Model of EEG Stimulation Source
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References
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A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Journal ArticleDOI
Removing electroencephalographic artifacts by blind source separation.
Tzyy-Ping Jung,Tzyy-Ping Jung,Scott Makeig,Colin Humphries,Te-Won Lee,Te-Won Lee,Martin J. McKeown,Vicente J. Iragui,Terrence J. Sejnowski,Terrence J. Sejnowski +9 more
TL;DR: The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
Journal ArticleDOI
Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state.
Ralph G. Andrzejak,Klaus Lehnertz,Florian Mormann,Christoph Rieke,Peter David,Christian E. Elger +5 more
TL;DR: Dynamical properties of brain electrical activity from different recording regions and from different physiological and pathological brain states are compared and strongest indications of nonlinear deterministic dynamics were found for seizure activity.
Journal ArticleDOI
Wavelet entropy: a new tool for analysis of short duration brain electrical signals.
Osvaldo A. Rosso,S. Blanco,Juliana Yordanova,Vasil Kolev,Alejandra Figliola,Martin Schürmann,Erol Başar +6 more
TL;DR: The major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals with specific quantifiers derived to characterize how stimulus affects electrical events in terms of frequency synchronization (tuning) in the event related potentials.