Multivariate Pattern Analysis Techniques for Electroencephalography Data to Study Flanker Interference Effects

López-García, David; Sobrado, Alberto; Peñalver, José M. G.; Górriz, Juan Manuel; Ruz, Marı́a

doi:10.1142/s0129065720500240

Cited by 12 publications

(15 citation statements)

References 73 publications

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“…As mentioned, we compiled this sample dataset for illustration purposes, including the EEG data of two main conditions (or classes) and four subconditions of three different participants. Readers interested on the results obtained for the entire sample should refer to the original publication [44].…”

Section: Resultsmentioning

confidence: 99%

“…Here, three different EEG data files have been selected from the original work [44,45]. For each participant, two different main conditions ( condition_a vs. condition_b ) have been selected for the MVPA analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, four subconditions ( condition_1, condition_2 , vs. condition_3 and condition_4 ) have been selected for the multivariate cross-classification analysis. Readers interested on the experimental details of these data should refer to the original publication [44,45]. During the original study, high-density EEG was recorded from 65 electrodes.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, an automatic trial rejection process was performed, pruning the data from non-stereotypical artifacts. For more details please see [44].…”

Section: Methodsmentioning

confidence: 99%

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MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

Lopez-Garia

Peñalver

Górriz³

et al. 2021

Preprint

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MVPAlab is a MATLAB-based and very flexible decoding toolbox for multidimensional electroencephalography and mag-netoencephalography data. The MVPAlab Toolbox implements several machine learning algorithms to compute multivari-ate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution anal-yses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrials generation. To draw statistical inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach. This toolbox has been designed to include an easy-to-use and very intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for those users with few or no previous coding experience. However, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Finally, an automatic trial rejection process was performed, pruning the data from non-stereotypical artifacts. For more details please see [44].…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

Lopez-Garia

Peñalver

Górriz³

et al. 2021

Preprint

Self Cite

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show abstract

“…Next, we performed automatic trial rejection to prune the data from non-stereotypical artifacts. It was based on out of a ±150μV range were automatically rejected (see [99][100][101] for similar preprocessing routines). The three methods in sum yielded an average of 8% of rejected trials per participant (range 1.8%-19%).…”

Section: Preprocessingmentioning

confidence: 99%

Top-down specific preparatory activations for Selective Attention and Perceptual Expectations

Peñalver

López-García

González-García

et al. 2022

Preprint

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SummaryProactive cognition brain models are mainstream nowadays. Within these, preparation is understood as an endogenous, top-down function that takes place prior to the actual perception of a stimulus and improves subsequent behavior. Neuroimaging has uncovered the existence of such preparatory activity in different cognitive domains, however no research to date has sought to uncover their potential similarities and differences. Two of these domains, often confounded in the literature, are Selective Attention (information relevance) and Perceptual Expectation (information probability). We used EEG to characterize the mechanisms that support preparation in Attention and Expectation. In different blocks, participants were cued to the relevance or to the probability of target categories, faces vs. names in a gender discrimination task. Multivariate Pattern (MVPA) and Representational Similarity Analyses (RSA) during the preparation window showed that both manipulations led to a significant, ramping-up prediction of the relevant or expected target category. However, classifiers trained on data from one condition did not generalize to the other, indicating the existence of different anticipatory neural patterns. Further analyses pointed to a differential involvement of specific frequency bands in each condition. Finally, a Canonical Template Tracking procedure showed that there was stronger perceptual reinstatement for Attention than for Expectation. Overall, results indicate that preparation during attention and expectation acts through different, although partially overlapping, neural mechanisms. These findings have important implications for current models of brain functioning, as they are a first step towards characterizing and dissociating the neural mechanisms involved in top-down anticipatory processing.

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Prolonged neural encoding of visual information in autism

Marsicano,

Casartelli,

Federici

et al. 2023

Autism Research

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Autism spectrum disorder (ASD) is associated with a hyper‐focused visual attentional style, impacting higher‐order social and affective domains. The understanding of such peculiarity can benefit from the use of multivariate pattern analysis (MVPA) of high‐resolution electroencephalography (EEG) data, which has proved to be a powerful technique to investigate the hidden neural dynamics orchestrating sensory and cognitive processes. Here, we recorded EEG in typically developing (TD) children and in children with ASD during a visuo‐spatial attentional task where attention was exogenously captured by a small (zoom‐in) or large (zoom‐out) cue in the visual field before the appearance of a target at different eccentricities. MVPA was performed both in the cue‐locked period, to reveal potential differences in the modulation of the attentional focus, and in the target‐locked period, to reveal potential cascade effects on stimulus processing. Cue‐locked MVPA revealed that while in the TD group the pattern of neural activity contained information about the cue mainly before the target appearance, the ASD group showed a temporally sustained and topographically diffuse significant decoding of the cue neural response even after the target onset, suggesting a delayed extinction of cue‐related neural activity. Crucially, this delayed extinction positively correlated with behavioral measures of attentional hyperfocusing. Results of target‐locked MVPA were coherent with a hyper‐focused attentional profile, highlighting an earlier and stronger decoding of target neural responses in small cue trials in the ASD group. The present findings document a spatially and temporally overrepresented encoding of visual information in ASD, which can constitute one of the main reasons behind their peculiar cognitive style.

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Multivariate Pattern Analysis Techniques for Electroencephalography Data to Study Flanker Interference Effects

Cited by 12 publications

References 73 publications

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

Top-down specific preparatory activations for Selective Attention and Perceptual Expectations

Prolonged neural encoding of visual information in autism

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