Electroencephalography

Biasiucci, Andrea; Franceschiello, Benedetta; Murray, Micah M.

doi:10.1016/j.cub.2018.11.052

Cited by 262 publications

(172 citation statements)

References 13 publications

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“…The accessibility of non-invasive human scalp recordings, such as electroencephalography (EEG) makes it one of the most widely used tools in the cognitive neuroscience research as well as in the clinical and commercial settings (Biasiucci, Franceschiello, & Murray, 2019). Its fine temporal resolution at millisecond-level grants this technique enormous advantages to probe the underlying neural dynamics of various cognitive functions (Luck, 2014).…”

Section: Introductionmentioning

confidence: 99%

Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

Wang¹,

Zhu²,

Tian³

2019

Preprint

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The fine temporal resolution of electroencephalography (EEG) makes it one of the most widely used non-invasive electrophysiological recording methods in cognitive neuroscience research.One of the common ways to explore the neural dynamics is to create event-related potentials (ERPs) by averaging trials, followed by the examination of the response magnitude at peak latencies. However, a complete profile of neural dynamics, including temporal indices of onset time, offset time, duration, and processing speed, is needed to investigate cognitive neural mechanisms. Based on the multivariate topographic analysis, we developed an analytical framework that included two methods to explore neural dynamics in ERPs. The first method separates continuous ERP waveforms into distinct components based on their topographic patterns. Crucial temporal indices such as the peak latency, onset and offset times can be automatically identified and indices about processing speed such as duration, rise, and fall speed can be derived. The second method scrutinizes the temporal dynamics of identified components by reducing the temporal variance among trials. The response peaks of signal trials are identified based on a target topographic template, and temporal-variance-free ERPs are obtained after aligning individual trials. This method quantifies the temporal variance as a new measure of cognitive noise, as well as increases both the accuracy of temporal dynamics estimation and the signal-to-noise ratio (SNR) of the ERP responses. The validity and reliability of these methods were tested with simulation as well as empirical datasets from an attention study and a semantic priming (N400) study. Together, we offer an analytical framework in a data-driven, bias-free manner to investigate neural dynamics in non-invasive scalp recordings. These methods are implemented in the Python-based open-source package TTT (Topography-based Temporalanalysis Toolbox).

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Section: Introductionmentioning

confidence: 99%

Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

Wang¹,

Zhu²,

Tian³

2019

Preprint

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“…feedback mechanisms from higher-level visual cortices into primary visual cortex), might improve our understanding of how visual neuronal processes create such images. Our computational and behavioral results could therefore be complemented by brain mapping and neuroimaging studies with high temporal resolution, such as EEG (Biasiucci, Franceschiello, & Murray, 2019).…”

Section: Discussionmentioning

confidence: 99%

A psychophysically-tuned computational model of human primary visual cortex produces geometric optical illusions

Retsa

Ariza

Noordanus

et al. 2020

Preprint

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AbstractGeometrical optical illusion (GOIs) are mismatches between physical stimuli and perception. GOIs provide an access point to study the interplay between sensation and perception, yet there is scant quantitative investigation of the extent to which different GOIs rely on similar or distinct brain mechanisms. We addressed this knowledge gap. First, 30 healthy adults reported quantitatively their perceptual biases with three GOIs, whose physical properties parametrically varied on a trial-by-trial basis. Biases observed with one GOI were unrelated to those observed with another GOI, suggestive of (partially) distinct underlying mechanisms. Next, we used these psychophysical results to tune a computational model of primary visual cortex that combines parameters of orientation, selectivity, intra-cortical connectivity, and long-range interactions. We showed that similar biases could be generated in-silico, mirroring those observed in humans. Such results provide a roadmap whereby computational modelling, informed by human psychophysics, can reveal likely mechanistic underpinnings of perception.

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“…In Carlson et al (2013), fMRI was used to measure brain activity in IT and the primary visual cortex, whereas Sassenhagen & Fiebach (2019) used EEG to capture whole-brain activity responses. These fundamentally different neuroimaging techniques and the brain regions being sampled mean that the neural representation captured could reasonably be said to differ from those used here, such that the spatial/ temporal trade-off in fMRI data means that one single fMRI image is likely to double the time as the entire time down-sampled in the current decoding and REPRESENTATIONAL SIMILARITY RSA analyses (Beres, 2017;Biasiucci, Franceschiello & Murray, 2019). Together, the issues of stimulus characteristics, pre-adopted data, and relating activity patterns captured by different modalities of brain-activity measurement (fRMI and EEG) each poses a challenge for reconciling the disparities in results from different studies.…”

Section: Weak Correspondence Between Distributed Semantic Representatmentioning

confidence: 99%

A Study of Representational Similarity: The Emergence of Object Concepts in Rapid Serial Visual Presentation Streams

Zhou

Grootswagers

Segula

et al. 2019

Preprint

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Electroencephalography

Cited by 262 publications

References 13 publications

Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

A psychophysically-tuned computational model of human primary visual cortex produces geometric optical illusions

A Study of Representational Similarity: The Emergence of Object Concepts in Rapid Serial Visual Presentation Streams

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