Spatially modulated alpha-band activity does not mediate tactile remapping and fast overt orienting behavior

Jp, Ossandón; König, Peter; Heed, Tobias

doi:10.1101/576850

Cited by 4 publications

(5 citation statements)

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“…Although most EEG datasets during free viewing have so far been analyzed in the time domain, it is also possible to study eye-movement-related changes in oscillatory power and phase ( Bodis-Wollner et al, 2002 ; Gaarder et al, 1966 ; Hutzler, Vignali, Hawelka, Himmelstoss, & Richlan, 2016 ; Kaiser, Brunner, Leeb, Neuper, & Pfurtscheller, 2009 ; Kornrumpf, Dimigen, & Sommer, 2017 ; Metzner, von der Malsburg, Vasishth, & Rösler, 2015 ; Nikolaev et al, 2016 ; Ossandón et al, 2010 ). Event-related responses in the frequency domain, such as induced changes in power, can last for several seconds and are likely biased by overlapping activity in much the same way as FRPs ( Litvak et al, 2013 ; Ossandón, König, & Heed, 2019 ). To address this problem, Ossandón and colleagues ( Ossandón et al, 2019 ) recently used the Hilbert transformation to obtain instantaneous power of the EEG in the alpha band.…”

Section: Discussionmentioning

confidence: 99%

“…Event-related responses in the frequency domain, such as induced changes in power, can last for several seconds and are likely biased by overlapping activity in much the same way as FRPs ( Litvak et al, 2013 ; Ossandón, König, & Heed, 2019 ). To address this problem, Ossandón and colleagues ( Ossandón et al, 2019 ) recently used the Hilbert transformation to obtain instantaneous power of the EEG in the alpha band. They then deconvolved the bandpass-filtered and rectified signal, showing that deconvolution can also be applied to EEG oscillations.…”

Section: Discussionmentioning

confidence: 99%

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Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Dimigen

Ehinger

2021

Journal of Vision

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Dimigen

Ehinger

2021

Journal of Vision

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“…One could also estimate the instantaneous power for induced potentials. For instance Ossandón, König & Heed (2019) deconvolved the instantaneous power after band pass filtering the alpha band. But this assumes that the overlap is linear in power.…”

Section: Discussionmentioning

confidence: 99%

Unfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis

Ehinger

Dimigen

2019

PeerJ

127

145

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Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a linear or non-linear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined electroencephalogram (EEG)/eye-tracking experiments during natural vision, fast multisensory stimulation experiments, and mobile brain/body imaging studies. However, even “traditional,” highly controlled ERP datasets often contain a hidden mix of overlapping activity (e.g., from stimulus onsets, involuntary microsaccades, or button presses) and it is helpful or even necessary to disentangle these components for a correct interpretation of the results. In this paper, we introduce unfold, a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses that combines existing concepts of massive univariate modeling (“regression-ERPs”), linear deconvolution modeling, and non-linear modeling with the generalized additive model into one coherent and flexible analysis framework. The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. It also includes advanced options for regularization and the use of temporal basis functions (e.g., Fourier sets). We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. In addition to traditional and non-conventional EEG/ERP designs, unfold can also be applied to other overlapping physiological signals, such as pupillary or electrodermal responses. It is available as open-source software at .

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“…To address this problem, Ossandón and colleagues (Ossandón, König, & Heed, 2019) recently used the Hilbert transformation to obtain instantaneous power of the EEG in the alpha band.…”

Section: Time-frequency Analysismentioning

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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Spatially modulated alpha-band activity does not mediate tactile remapping and fast overt orienting behavior

Cited by 4 publications

References 50 publications

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Unfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis

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

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