Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming

Craddock, Matt; Martinović, Jasna; Müller, Matthias

doi:10.1111/psyp.12593

Cited by 19 publications

(18 citation statements)

References 34 publications

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“…In the visual domain, earlier work on basic stimuli, investigating GBRs to coherently (i.e., parallel) vs. incoherently moving bars (Gray and Singer, 1989; Gray et al, 1989; Engel et al, 1991a,b) in animals was closely followed by cognitive investigations, with real object pictures eliciting greater GBRs than pictures of unrecognizable, fragmented or scrambled objects or faces (Tallon-Baudry et al, 1996; Gruber et al, 2002; Henson et al, 2009; Hassler et al, 2011; Bertrand et al, 2013; Gao et al, 2013; Craddock et al, 2015). Although, Yuval-Greenberg and colleagues (Yuval-Greenberg et al, 2008) showed that induced gamma-band activity (iGBA) in neurophysiological data can be contaminated by artifacts originating from miniature saccades or muscle activity, we note that: (1) several of these results can hardly be attributed to effects of microsaccades, as, for example, these studies controlled for the physical features of the stimuli (Gruber et al, 2002), presented stimuli tachistoscopically so that eye movements were discouraged or excluded muscle artifacts based on EMG recordings (Pulvermüller et al, 1997), or used intracortical recording methods (or magnetoencephalography, MEG) (Bertrand et al, 2013; Gao et al, 2013), which are minimally affected by small eye artifacts; (2) some evidence suggests that microsaccades actually decrease when looking at a coherent stimulus as compared to an incoherent one (Makin et al, 2011); and (3) the use of artifact-removing methods such as independent component analysis and beamforming (Keren et al, 2010; Craddock et al, 2016) enables identifying iGBA activity increases in the signal even after removal of miniature-saccade effects (Hassler et al, 2011, 2013; Craddock et al, 2015). …”

Section: Discussionmentioning

confidence: 99%

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

Garagnani

Lucchese

Tomasello

et al. 2017

Front. Comput. Neurosci.

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Experimental evidence indicates that neurophysiological responses to well-known meaningful sensory items and symbols (such as familiar objects, faces, or words) differ from those to matched but novel and senseless materials (unknown objects, scrambled faces, and pseudowords). Spectral responses in the high beta- and gamma-band have been observed to be generally stronger to familiar stimuli than to unfamiliar ones. These differences have been hypothesized to be caused by the activation of distributed neuronal circuits or cell assemblies, which act as long-term memory traces for learned familiar items only. Here, we simulated word learning using a biologically constrained neurocomputational model of the left-hemispheric cortical areas known to be relevant for language and conceptual processing. The 12-area spiking neural-network architecture implemented replicates physiological and connectivity features of primary, secondary, and higher-association cortices in the frontal, temporal, and occipital lobes of the human brain. We simulated elementary aspects of word learning in it, focussing specifically on semantic grounding in action and perception. As a result of spike-driven Hebbian synaptic plasticity mechanisms, distributed, stimulus-specific cell-assembly (CA) circuits spontaneously emerged in the network. After training, presentation of one of the learned “word” forms to the model correlate of primary auditory cortex induced periodic bursts of activity within the corresponding CA, leading to oscillatory phenomena in the entire network and spontaneous across-area neural synchronization. Crucially, Morlet wavelet analysis of the network's responses recorded during presentation of learned meaningful “word” and novel, senseless “pseudoword” patterns revealed stronger induced spectral power in the gamma-band for the former than the latter, closely mirroring differences found in neurophysiological data. Furthermore, coherence analysis of the simulated responses uncovered dissociated category specific patterns of synchronous oscillations in distant cortical areas, including indirectly connected primary sensorimotor areas. Bridging the gap between cellular-level mechanisms, neuronal-population behavior, and cognitive function, the present model constitutes the first spiking, neurobiologically, and anatomically realistic model able to explain high-frequency oscillatory phenomena indexing language processing on the basis of dynamics and competitive interactions of distributed cell-assembly circuits which emerge in the brain as a result of Hebbian learning and sensorimotor experience.

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Section: Discussionmentioning

confidence: 99%

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

Garagnani

Lucchese

Tomasello

et al. 2017

Front. Comput. Neurosci.

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“…Previous co-registration studies have shown that despite their small size, miniature saccades can generate sizable eye muscle (Craddock et al, 2016;Yuval-Greenberg & Deouell, 2009) and brain potentials (Dimigen et al, 2009;Gaarder et al, 1964;Meyberg et al, 2015) in EEG. Furthermore, because the amplitude and rate of miniature saccades often differs systematically between experimental conditions (Rolfs, 2009), these additional signals can seriously distort stimulus-locked analyses in the time and frequency domain (Dimigen et al, 2009;Yuval-Greenberg et al, 2008).…”

Section: Experiments 1: Face Perceptionmentioning

confidence: 99%

“…Although some residual artifacts (in particular from spike potentials) are visible in the waveforms of most published FRP studies, it seems likely that correction procedures can be improved further in the future, for example, by taking into account the information provided by the eye-tracker. Specifically, the concurrent eye-tracking data is useful to select optimal training data for the ICA algorithm (Craddock, Martinovic, & Müller, 2016;Keren et al, 2010), to identify artifact components (Plöchl et al, 2012), and to evaluate the results of the correction (Dimigen, 2020;Ries et al, 2018b). Recent findings indicate that if ICA procedures are fine-tuned for free viewing experiments in this manner, ocular artifacts can be almost fully removed with relatively little distortion of genuine brain activity (Dimigen, 2020;Ries et al, 2018b).…”

Section: Introductionmentioning

confidence: 99%

Analyzing combined eye-tracking/EEG experiments with (non)linear deconvolution models

Dimigen¹,

Ehinger

2019

Preprint

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Fixation-related potentials (FRPs), neural responses aligned to saccade offsets, are a promising tool to study the dynamics of attention and cognition under natural viewing conditions. In the past, four methodological problems have complicated the analysis of such combined eye-tracking/EEG experiments: (i) the synchronization of data streams, (ii) the removal of ocular artifacts, (iii) the condition-specific temporal overlap between the brain responses evoked by consecutive fixations, (iv) and the fact that numerous low-level stimulus and saccade properties also influence the post-saccadic neural responses. While effective solutions exist for the first two problems, the latter ones are only beginning to be addressed. In the current paper, we present and review a unified framework for FRP analysis that allows us to deconvolve overlapping potentials and control for linear and nonlinear confounds on theFRPs. An open software implementation is provided for all procedures. We then demonstrate the advantages of this approach for data from three commonly studied paradigms: face perception, scene viewing, and natural sentence reading. First, for a traditional ERP face recognition experiment, we show how deconvolution can separate stimulus-ERPs from overlapping muscle and brain potentials produced by small (micro)saccades on the face.Second, in scene viewing, we isolate multiple non-linear influences of saccade parameters on the FRP. Finally, for a natural sentence reading experiment using the boundary paradigm, we show how it is possible to study the neural correlates of parafoveal preview after removing spurious overlap effects caused by the associated difference in average fixation time. Our results suggest a principal way of measuring reliable fixation-related brain potentials during natural vision.

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“…during attempted fixation generate sizeable SPs (Armington, 1978;Dimigen, Valsecchi, Sommer, & Kliegl, 2009;Yamazaki, 1968;Yuval-Greenberg, Tomer, Keren, Nelken, & Deouell, 2008), which introduce a broad band artifact in the time-frequency spectrum of the EEG, affecting the low-amplitude beta and gamma bands, in particular (Reva & Aftanas, 2004;Yuval-Greenberg et al, 2008). Complete removal of SPs with ICA has proven challenging even for microsaccades (Craddock, Martinovic, & Müller, 2016;Hassler, Barreto, & Gruber, 2011;Hipp & Siegel, 2013;Keren et al, 2010) and ICA often fails to single out the SP in one or more distinct ICs (Hipp & Siegel, 2013;Keren et al, 2010). In addition, even clean SPs components can be difficult to spot in scalp maps (inverse weights), especially if EOG electrodes were not placed around both eyes, recorded with a bipolar montage, or excluded before ICA.…”

Section: Spike Potentials Have Received Attention Because Even Involumentioning

confidence: 99%

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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Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming

Abstract: Article:Craddock, M, Martinovic, J and Müller, MM (2016) Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming. Psychophysiology, 53 (4).

Cited by 19 publications

References 34 publications

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

Analyzing combined eye-tracking/EEG experiments with (non)linear deconvolution models

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

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