Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals

Barbati, Giulia; Porcaro, Camillo; Zappasodi, Filippo; Rossini, Paolo Maria; Tecchio, Franca

doi:10.1016/j.clinph.2003.12.015

Cited by 273 publications

(217 citation statements)

References 14 publications

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“…On the basis of that feature, it is theoretically not enough to eliminate several components with the highest values. The components corresponding to particular artefacts should be found and removed [15]. The research confirmed this statement: the average number of rejected independent components was significantly higher than in the case of PCA [12,17].…”

Section: The Case Studysupporting

confidence: 70%

“…However, the identification of a component as the artefact is more complicated. The localisation of the component, as well as its character, should also be analysed [15]. Another inconvenience was the fact that it was necessary to delete a comparatively larger number of components (6 IC compared to 1-2 PC).…”

Section: Discussionmentioning

confidence: 99%

“…There is a possibility to subtract the signal including the artefacts originating from the electrooculography activity. It may reduce artefacts in a significant way [9,14,15].…”

Section: Methods Of Artefact Correctionmentioning

confidence: 99%

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Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Kaczorowska

2017

ITM Web Conf.

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Abstract. The present paper covers the review of artefacts of EEG recording, as well as methods to eliminate them, i.e. PCA and ICA. The reviewed artefacts were of various origins: biological (muscle, eye blinking, eyeball moving), as well as technological artefacts caused mainly by the power network with the frequency of 50 Hz. The research has been conducted on a group consisting of 8 students. The research was carried out with the use of two defined models of electroencephalographs: stationary and mobile, then results have been compared. The comparison of two amplifiers combined with various artefact elimination methods has been presented. Presented research has shown that the record performed by the mobile amplifier contains the artefacts of significantly lower amplitude.

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Section: The Case Studysupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

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Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Kaczorowska

2017

ITM Web Conf.

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“…We compared five different methods for detecting trials containing artifacts (Barbati et al, 2004;Delorme et al, 2001):…”

Section: Methods Of Artifact Detectionmentioning

confidence: 99%

“…Each data sample point was thus associated with a probability. Then, we computed the joint log probability J e (i) of the activity values (A i ) in each data trial i and electrode e by (1) where p D e , (x) is the probability of observing the value x in the probability distribution D e of activity at electrode e. We used the joint log probability for more effective graphic presentation of very low joint probability values. The joint probability was computed for every data trial at each electrode or independent component.…”

Section: Linear Trendsmentioning

confidence: 99%

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

2007

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Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (−50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICAdecomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed nonartifact processes themselves.

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Functional source separation from magnetoencephalographic signals

Barbati

Sigismondi

Zappasodi

et al. 2006

Human Brain Mapping

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We propose a novel cerebral source extraction method (functional source separation, FSS) starting from extra-cephalic magnetoencephalographic (MEG) signals in humans. It is obtained by adding a functional constraint to the cost function of a basic independent component analysis (ICA) model, defined according to the specific experiment under study, and removing the orthogonality constraint, (i.e., in a single-unit approach, skipping decorrelation of each new component from the subspace generated by the components already found). Source activity was obtained all along processing of a simple separate sensory stimulation of thumb, little finger, and median nerve. Being the sources obtained one by one in each stage applying different criteria, the a posteriori "interesting sources selection" step is avoided. The obtained solutions were in agreement with the homuncular organization in all subjects, neurophysiologically reacting properly and with negligible residual activity. On this basis, the separated sources were interpreted as satisfactorily describing highly superimposed and interconnected neural networks devoted to cortical finger representation. The proposed procedure significantly improves the quality of the extraction with respect to a standard BSS algorithm. Moreover, it is very flexible in including different functional constraints, providing a promising tool to identify neuronal networks in very general cerebral processing.

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Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals

Cited by 273 publications

References 14 publications

Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Functional source separation from magnetoencephalographic signals

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