Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial

[1]  D. Baldauf,et al.  Decoding Object-Based Auditory Attention from Source-Reconstructed MEG Alpha Oscillations , 2021, The Journal of Neuroscience.

[2]  G. Dehaene-Lambertz,et al.  Orthogonal neural codes for speech in the infant brain , 2021, Proceedings of the National Academy of Sciences.

[3]  Giancarlo Valente,et al.  Cross-validation and permutations in MVPA: Validity of permutation strategies and power of cross-validation schemes , 2021, NeuroImage.

[4]  Patrick Cavanagh,et al.  Decoding the Temporal Dynamics of Covert Spatial Attention Using Multivariate EEG Analysis: Contributions of Raw Amplitude and Alpha Power , 2020, Frontiers in Human Neuroscience.

[5]  Radoslaw Martin Cichy,et al.  Dissociable components of oscillatory activity underly information encoding in human perception , 2020, bioRxiv.

[6]  Hi-Joon Park,et al.  Spatial Information of Somatosensory Stimuli in the Brain: Multivariate Pattern Analysis of Functional Magnetic Resonance Imaging Data , 2020, Neural plasticity.

[7]  Radoslaw Martin Cichy,et al.  Visual Imagery and Perception Share Neural Representations in the Alpha Frequency Band , 2020, Current Biology.

[8]  C. Nelson,et al.  Auditory Processing of Speech and Tones in Children With Tuberous Sclerosis Complex , 2020, Frontiers in Integrative Neuroscience.

[9]  Mark H. Johnson,et al.  Language Experience Impacts Brain Activation for Spoken and Signed Language in Infancy: Insights From Unimodal and Bimodal Bilinguals , 2020, Neurobiology of Language.

[10]  Radoslaw Martin Cichy,et al.  Temporal dynamics of visual representations in the infant brain , 2020, Developmental Cognitive Neuroscience.

[11]  Rhett N. D’souza,et al.  Structural Analysis and Optimization of Convolutional Neural Networks with a Small Sample Size , 2020, Scientific Reports.

[12]  Thomas F Münte,et al.  Quantifying the individual auditory and visual brain response in 7-month-old infants watching a brief cartoon movie , 2019, NeuroImage.

[13]  Yury Shtyrov,et al.  MVPA Analysis of Intertrial Phase Coherence of Neuromagnetic Responses to Words Reliably Classifies Multiple Levels of Language Processing in the Brain , 2019, eNeuro.

[14]  Jonas Obleser,et al.  Quantifying the individual auditory and visual brain response in 7- month-old infants watching a brief cartoon movie , 2019, bioRxiv.

[15]  L. Nickels,et al.  Towards an individualised neural assessment of receptive language in children , 2019, bioRxiv.

[16]  Caspar G. Chorus,et al.  Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis , 2018, Journal of Choice Modelling.

[17]  B. Balas,et al.  Sensitivity to face animacy and inversion in childhood: Evidence from EEG data , 2021, Neuropsychologia.

[18]  Laurie Bayet,et al.  Sensitivity to face animacy and inversion in childhood: Evidence from event-related potentials and time-resolved classification of EEG data , 2018 .

[19]  Elia Formisano,et al.  Methods for computing the maximum performance of computational models of fMRI responses , 2018, bioRxiv.

[20]  Benjamin Blankertz,et al.  Classifying the mental representation of word meaning in children with Multivariate Pattern Analysis of fNIRS , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[21]  Caitlin Tenison,et al.  Practices and pitfalls in inferring neural representations , 2018, NeuroImage.

[22]  Masashi Sato,et al.  Information spreading by a combination of MEG source estimation and multivariate pattern classification , 2018, PloS one.

[23]  Radoslaw Martin Cichy,et al.  Multivariate pattern analysis for MEG: A comparison of dissimilarity measures , 2018, NeuroImage.

[24]  N. Turk-Browne,et al.  Infant fMRI: A Model System for Cognitive Neuroscience , 2018, Trends in Cognitive Sciences.

[25]  Benjamin D Zinszer,et al.  Decoding semantic representations from functional near-infrared spectroscopy signals , 2017, Neurophotonics.

[26]  Radoslaw Martin Cichy,et al.  Multivariate pattern analysis for MEG: a comprehensive comparison of dissimilarity measures , 2017, bioRxiv.

[27]  Richard N. Aslin,et al.  Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS , 2017, PloS one.

[28]  Jörn Diedrichsen,et al.  Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis , 2017, bioRxiv.

[29]  Jörn Diedrichsen,et al.  Reliability of dissimilarity measures for multi-voxel pattern analysis , 2016, NeuroImage.

[30]  Andres Hoyos Idrobo,et al.  Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines , 2016, NeuroImage.

[31]  Susan G. Wardle,et al.  Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data , 2016, Journal of Cognitive Neuroscience.

[32]  Benjamin D. Zinszer,et al.  Representational similarity encoding for fMRI: Pattern-based synthesis to predict brain activity using stimulus-model-similarities , 2016, NeuroImage.

[33]  John-Dylan Haynes,et al.  Valid population inference for information-based imaging: From the second-level t-test to prevalence inference , 2015, NeuroImage.

[34]  G. Dehaene-Lambertz,et al.  The Infancy of the Human Brain , 2015, Neuron.

[35]  Thomas Naselaris,et al.  Resolving Ambiguities of MVPA Using Explicit Models of Representation , 2015, Trends in Cognitive Sciences.

[36]  S. Luck,et al.  How inappropriate high-pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition. , 2015, Psychophysiology.

[37]  Kyungmin Su,et al.  The PREP pipeline: standardized preprocessing for large-scale EEG analysis , 2015, Front. Neuroinform..

[38]  Xintao Hu,et al.  Decoding Semantics Categorization during Natural Viewing of Video Streams , 2015, IEEE Transactions on Autonomous Mental Development.

[39]  Alexandra Woolgar,et al.  Multi-voxel pattern analysis (MVPA) reveals abnormal fMRI activity in both the “core” and “extended” face network in congenital prosopagnosia , 2014, Front. Hum. Neurosci..

[40]  J. S. Guntupalli,et al.  Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.

[41]  Steven J. Luck,et al.  ERPLAB: an open-source toolbox for the analysis of event-related potentials , 2014, Front. Hum. Neurosci..

[42]  Li Su,et al.  A Toolbox for Representational Similarity Analysis , 2014, PLoS Comput. Biol..

[43]  Martin Luessi,et al.  MEG and EEG data analysis with MNE-Python , 2013, Front. Neuroinform..

[44]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

[45]  Joachim M. Buhmann,et al.  Decoding the perception of pain from fMRI using multivariate pattern analysis , 2012, NeuroImage.

[46]  James V. Haxby,et al.  Multivariate pattern analysis of fMRI: The early beginnings , 2012, NeuroImage.

[47]  Martha Ann Bell,et al.  Using EEG to Study Cognitive Development: Issues and Practices , 2012, Journal of cognition and development : official journal of the Cognitive Development Society.

[48]  N. Gaab,et al.  Pediatric neuroimaging in early childhood and infancy: challenges and practical guidelines , 2012, Annals of the New York Academy of Sciences.

[49]  Stefanie Hoehl,et al.  Recording Infant ERP Data for Cognitive Research , 2012, Developmental neuropsychology.

[50]  Richard Granger,et al.  Investigation of melodic contour processing in the brain using multivariate pattern-based fMRI , 2011, NeuroImage.

[51]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[52]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[53]  Robert Oostenveld,et al.  Identifying Object Categories from Event-Related EEG: Toward Decoding of Conceptual Representations , 2010, PloS one.

[54]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

[55]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[56]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[57]  G. Rees,et al.  Neuroimaging: Decoding mental states from brain activity in humans , 2006, Nature Reviews Neuroscience.

[58]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[59]  R. Aslin,et al.  Methodological challenges for understanding cognitive development in infants , 2005, Trends in Cognitive Sciences.

[60]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[61]  S. Dehaene,et al.  Functional Neuroimaging of Speech Perception in Infants , 2002, Science.

[62]  Laura Gwilliams,et al.  Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition , 2017 .

[63]  Joel Z. Leibo,et al.  The dynamics of invariant object recognition in the human visual system. , 2014, Journal of neurophysiology.

[64]  Mahesh Panchal,et al.  A Review on Support Vector Machine for Data Classification , 2012 .

[65]  Trevor Hastie,et al.  Linear Methods for Classification , 2001 .

[66]  G. Berns Functional neuroimaging. , 1999, Life sciences.