Multivariate Pattern Analysis of Electroencephalography Data in a Demand-Selection Task

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

doi:10.1007/978-3-030-19591-5_41

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…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%

“…A sample EEG dataset has been compiled to test all the MVPAlab main functionalities. It is freely available in the following repository: https://osf.io/du6fa/ Here, three different EEG data files have been selected from the original work [43,44]. For each participant, two different main conditions (condition_a vs. condition_b) have been selected for the MVPA analysis.…”

Section: Sample Eeg Datasetmentioning

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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Sample Eeg Datasetmentioning

confidence: 99%

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MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

Lopez-Garia

Peñalver

Górriz³

et al. 2021

Preprint

Self Cite

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“…processing Classical EEG signal processing techniques along with multivariate pattern analysis is used in [135] to decode conflict-related neural processes associated with congruent or incongruent events in a timefrequency resolved way and determined how different frequency bands contribute to the overall decoding accuracy by means of a linear SVM.…”

Section: Multivariate Pattern Analysis In Eeg Signalmentioning

confidence: 99%

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

et al. 2020

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Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in

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Representational Similarity Analysis: A Preliminary Step to fMRI-EEG Data Fusion in MVPAlab

López-García

González-Peñalver

Górriz

et al. 2022

Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications

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Multivariate Pattern Analysis of Electroencephalography Data in a Demand-Selection Task

Cited by 4 publications

References 14 publications

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

Representational Similarity Analysis: A Preliminary Step to fMRI-EEG Data Fusion in MVPAlab

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