Single-trial evoked potential estimation: Comparison between independent component analysis and wavelet denoising

Iyer, Darshan; Zouridakis, George

doi:10.1016/j.clinph.2006.10.024

Cited by 68 publications

(47 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Given the importance of response sensitivity at a single-trial level for simultaneous EEG-fMRI, it is desirable to further explore this question in future work, for example by comparing responses to checkerboards of different FOV/contrast, and exploring alternative denoising techniques such as iterative ICA (Iyer and Zouridakis, 2007), wavelet-based approaches (Quian Quiroga and Garcia, 2003), or beamformer methodologies .…”

Section: Simultaneous Eeg-fmri Acquisitionsmentioning

confidence: 99%

Simultaneous EEG–fMRI at ultra-high field: Artifact prevention and safety assessment

et al. 2015

View full text Add to dashboard Cite

The simultaneous recording of scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can provide unique insights into the dynamics of human brain function, and the increased functional sensitivity offered by ultra-high field fMRI opens exciting perspectives for the future of this multimodal approach. However, simultaneous recordings are susceptible to various types of artifacts, many of which scale with magnetic field strength and can seriously compromise both EEG and fMRI data quality in recordings above 3 T. The aim of the present study was to implement and characterize an optimized setup for simultaneous EEG-fMRI in humans at 7 T. The effects of EEG cable length and geometry for signal transmission between the cap and amplifiers were assessed in a phantom model, with specific attention to noise contributions from the MR scanner coldheads. Cable shortening (down to 12 cm from cap to amplifiers) and bundling effectively reduced environment noise by up to 84% in average power and 91% in inter-channel power variability. Subject safety was assessed and confirmed via numerical simulations of RF power distribution and temperature measurements on a phantom model, building on the limited existing literature at ultra-high field. MRI data degradation effects due to the EEG system were characterized via B 0 and B 1 + field mapping on a human volunteer, demonstrating important, although not prohibitive, B 1 disruption effects. With the optimized setup, simultaneous EEG-fMRI acquisitions were performed on 5 healthy volunteers undergoing two visual paradigms: an eyes-open/eyes-closed task, and a visual evoked potential (VEP) paradigm using reversing-checkerboard stimulation. EEG data exhibited clear occipital alpha modulation and average VEPs, respectively, with concomitant BOLD signal changes. On a single-trial level, alpha power variations could be observed with relative confidence on all trials; VEP detection was more limited, although statistically significant responses could be detected in more than 50% of trials for every subject. Overall, we conclude that the proposed setup is well suited for simultaneous EEG-fMRI at 7 T.

show abstract

Section: Simultaneous Eeg-fmri Acquisitionsmentioning

confidence: 99%

Simultaneous EEG–fMRI at ultra-high field: Artifact prevention and safety assessment

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Ensemble averages are computed for every cluster and compared with the corresponding ensemble average of visually selected trials. Note that in spite of not being optimal, the ensemble average waveform is often the goal in practice or it constitutes a reference signal [11,7] of a single-trial enhancement algorithm. The data set analyzed comprises 32 sessions (two sessions per participant) with roughly 250 trials per session.…”

Section: Evoked Response Potential Signalsmentioning

confidence: 99%

SSA of biomedical signals: A linear invariant systems approach

Figueiredo

Georgieva

Lang

et al. 2010

Statistics and Its Interface

View full text Add to dashboard Cite

Singular spectrum analysis (SSA) is considered from a linear invariant systems perspective. In this terminology, the extracted components are considered as outputs of a linear invariant system which corresponds to finite impulse response (FIR) filters. The number of filters is determined by the embedding dimension. We propose to explicitly define the frequency response of each filter responsible for the selection of informative components. We also introduce a subspace distance measure for clustering subspace models. We illustrate the methodology by analyzing Electroencephalograms (EEG).

show abstract

“…More recently, another BSS approach called Canonical Correlation Analysis (CCA) was also proposed to remove muscle artifacts from EEG and improve the interpretation of ictal epochs [14][15][16]. Regarding the wavelets transform (WT), it has been used in the EEG context for the detection of epileptiform patterns [17], for the elimination of different types of noises [18,19] and for removal of some electrophysiological artifacts, such as ocular movement [20,21], and heart signal [20,22]. Finally, a fourth method called Empirical Mode Decomposition (EMD) has newly appeared as a promising tool in the particular field of EEG data denoising [23].…”

Section: Introductionmentioning

confidence: 99%

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Safieddine

Kachenoura

Albera

et al. 2012

EURASIP J. Adv. Signal Process.

151

122

View full text Add to dashboard Cite

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

show abstract

Single-trial evoked potential estimation: Comparison between independent component analysis and wavelet denoising

Cited by 68 publications

References 26 publications

Simultaneous EEG–fMRI at ultra-high field: Artifact prevention and safety assessment

Simultaneous EEG–fMRI at ultra-high field: Artifact prevention and safety assessment

SSA of biomedical signals: A linear invariant systems approach

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Contact Info

Product

Resources

About