Influence of Signal Preprocessing on ICA-Based EEG Decomposition

Zakeri, Zohreh; Assecondi, Sara; Bagshaw, Andrew P.; Arvanitis, Theodoros N.

doi:10.1007/978-3-319-00846-2_182

Cited by 21 publications

(23 citation statements)

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“…As low-frequency aspects of the EEG signal typically account for large proportions of variance, the application of a high-pass filter can improve the ICA decomposition by removing slow drifts and DC components (Winkler, Debener, Muller, & Tangermann, 2015; Zakeri, Assecondi, Bagshaw, & Arvanitis, 2014). Similarly, including electrode channels located near the eye can improve the ICA decomposition for the purposes of artifact removal, as the electrodes provide greater information for the ICA algorithm to separate the artifact from the background EEG (Zakeri et al, 2014). …”

Section: Overall Discussionmentioning

confidence: 99%

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

et al. 2016

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Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies.

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Section: Overall Discussionmentioning

confidence: 99%

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

et al. 2016

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“…The reliability of ICA decompositions has been shown to improve after signal offsets are removed by subtracting the mean voltages across each epoch (mean-centering, Groppe, Makeig, & Kutas, 2009). In addition, practical experience suggests that decompositions improve if slow oscillations and drift are further suppressed by high-pass filtering (Viola, Debener, Thorne, & Schneider, 2010;Winkler, Debener, Muller, & Tangermann, 2015, see also Miyakoshi, 2018;Zakeri, Assecondi, Bagshaw, & Arvanitis, 2014). The adverse effects of slow signals on unmixing quality are not fully understood (Viola et al, 2010;Winkler et al, 2015), but one likely reason is that ICA is biased towards these highamplitude signals since it tends to focus on data expressing the most power.…”

Section: Explored Parameters Of the Ica Pipelinementioning

confidence: 99%

“…Removing high frequencies can also improve decompositions since it attenuates electromagnetic noise and scalp-EMG; cutoffs around 40-45 Hz are therfore commonly applied to ICA input data (e.g. Castellanos & Makarov, 2006;Gwin, Gramann, Makeig, & Ferris, 2010;Mannan, Kim, Jeong, & Kamran, 2016;Winkler et al, 2015;Zakeri et al, 2014). However, EMG is not only produced by face, head, and neck muscles, but presumably also reflected in the SP, whose bandwidth extends to at least 90 Hz (Keren et al, 2010;Nativ, Weinstein, & Rosas-Ramos, 1990).…”

Section: Explored Parameters Of the Ica Pipelinementioning

confidence: 99%

“…The preprocessing of the input data, in particular, has received comparatively little attention, but has been shown to be more important than the choice of ICA algorithm (e.g. Infomax, FastICA, or AMICA; Zakeri et al, 2014). Rather than proposing yet another correction method, goal of the present study was therefore to validate and adapt the pipeline for the widely-used extended Infomax algorithm (Bell & Sejnowski, 1995;Lee, Girolami, & Sejnowski, 1999) to natural viewing.…”

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Optimized ICA-based removal of ocular EEG artifacts from free viewing experiments

Dimigen¹,

Berlin²

2018

Preprint

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Author Note. I would like to acknowledge Maarten De Schuymer, who conducted early explorations of the effects of high-pass filtering that helped to initiate the current work. I am also grateful to Lisa Spiering for assisting with the MSEC correction and Werner Sommer for providing an excellent working environment. Collection of one of the datasets was supported by a grant from DFG (FG-868-A2). Comments or corrections are highly appreciated. ICA of free viewing EEG ABSTRACTCombining EEG with eye-tracking is a promising approach to study neural correlates of natural vision, but the resulting recordings are also heavily contaminated by activity of the eye balls, eye lids, and extraocular muscles. While Independent Component Analysis (ICA) is commonly used to suppress these ocular artifacts, its performance under free viewing conditions has not been systematically evaluated and many published findings display residual artifacts. Here I evaluated and optimized ICA-based correction for two tasks with unconstrained eye movements: visual search in images and sentence reading. In a first step, four parameters of the ICA pipeline were systematically varied: the (1) high-pass and (2) low-pass filter applied to the training data, (3) the proportion of training data containing myogenic saccadic spike potentials (SP), and (4) the threshold for eye tracker-based component rejection. In a second step, the eyetracker was used to objectively quantify correction quality of each ICA solution, both in terms of undercorrection (residual artifacts) and overcorrection (removal of neurogenic activity). As a benchmark, results were compared to those obtained with an alternative spatial filter, Multiple Source Eye Correction (MSEC). With commonly used settings, Infomax ICA not only left artifacts in the data of both tasks, but also distorted neurogenic activity during eye movementfree intervals. However, correction could be drastically improved by training the ICA on optimally filtered data in which SPs were massively overweighted. With optimized procedures, ICA removed virtually all artifacts, including the SP and its associated spectral broadband artifact, with little distortion of neural activity. It also outperformed MSEC in terms of SP correction. Matlab code is provided.

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“…FastICA is a much mature algorithm developed from blind source technology. FastICA simplifies the problem and operation which greatly improves the work efficiency, it is widely used in the analysis and processing of EEG [14,15].The structure between the interical and preictal data are different which means that the source signal components are also different,so it will be possible to distinguish between the interical and preictal data by FastICA. FastICA algorithm has many forms like basing on maximum likelihood estimation, negative entropy and so on.…”

Section: Fastica Algorithmmentioning

confidence: 99%

Extraction and Analysis of Independent Components in Interictal and Preictal Data

2017

2017 6th International Conference on Advanced Materials and Computer Science (ICAMCS 2017)

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Influence of Signal Preprocessing on ICA-Based EEG Decomposition

Cited by 21 publications

References 9 publications

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

Optimized ICA-based removal of ocular EEG artifacts from free viewing experiments

Extraction and Analysis of Independent Components in Interictal and Preictal Data

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