Parametric study of EEG sensitivity to phase noise during face processing - Wikidata - awgldk - TriplyDB

Parametric study of EEG sensitivity to phase noise during face processing

Parametric study of EEG sensitivity to phase noise during face processing

http://www.wikidata.org/entity/Q33373831

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Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories Early ERPs to faces: aging, luminance, and individual differences Low-Level Contrast Statistics of Natural Images Can Modulate the Frequency of Event-Related Potentials (ERP) in Humans Age-related delay in information accrual for faces: evidence from a parametric, single-trial EEG approach.Early Visually Evoked Electrophysiological Responses Over the Human Brain (P1, N170) Show Stable Patterns of Face-Sensitivity from 4 years to Adulthood.The background of reduced face specificity of N170 in congenital prosopagnosia.LIMO EEG: a toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic data.Stimulus dependency of object-evoked responses in human visual cortex: an inverse problem for category specificity.Optimal eye-gaze fixation position for face-related neural responses How the visual cortex handles stimulus noise: insights from amblyopia Working memory updating function training influenced brain activity Healthy aging delays scalp EEG sensitivity to noise in a face discrimination task.An objective method for measuring face detection thresholds using the sweep steady-state visual evoked response From perception to conception: how meaningful objects are processed over time.Visual processing during natural reading.Structural face encoding: How task affects the N170's sensitivity to race.Phase noise reveals early category-specific modulation of the event-related potentials.It's about Time.Four common conceptual fallacies in mapping the time course of recognition.Visual Object Categorization in the Brain: What Can We Really Learn from ERP Peaks?Quantifying the Time Course of Visual Object Processing Using ERPs: It's Time to Up the Game.The Deceptively Simple N170 Reflects Network Information Processing Mechanisms Involving Visual Feature Coding and Transfer Across Hemispheres.Regression-based estimation of ERP waveforms: I. The rERP framework.Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict.Modeling Single-Trial ERP Reveals Modulation of Bottom-Up Face Visual Processing by Top-Down Task Constraints (in Some Subjects).Working Memory Updating Training Improves Mathematics Performance in Middle School Students With Learning Difficulties.

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P2860

Parametric study of EEG sensitivity to phase noise during face processing

http://www.wikidata.org/entity/Q33373831

description

2008 nî lūn-bûn

@nan

2008 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2008 թվականի հոտեմբերին հրատարակված գիտական հոդված

@hy

2008年の論文

@ja

2008年論文

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2008年論文

@zh-hant

2008年論文

@zh-hk

2008年論文

@zh-mo

2008年論文

@zh-tw

2008年论文

@wuu

name

Parametric study of EEG sensitivity to phase noise during face processing

@ast

Parametric study of EEG sensitivity to phase noise during face processing

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type

label

Parametric study of EEG sensitivity to phase noise during face processing

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Parametric study of EEG sensitivity to phase noise during face processing

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prefLabel

Parametric study of EEG sensitivity to phase noise during face processing

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Parametric study of EEG sensitivity to phase noise during face processing

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1471-2202-9-S1-P98

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BMC Neuroscience

P1476

Parametric study of EEG sensitivity to phase noise during face processing

@en

P2093

Allison B Sekuler

http://www.w3.org/2001/XMLSchema#string

Patrick J Bennett

http://www.w3.org/2001/XMLSchema#string

P2860

Natural image statistics and neural representation

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

Visual Object Recognition

A human intracranial study of long-range oscillatory coherence across a frontal-occipital-hippocampal brain network during visual object processing.

Visual neuroscience: Resonating to natural images.

The time course of visual word recognition as revealed by linear regression analysis of ERP data.

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis.

Source analysis of the N170 to faces and objects.

Sparse coding of sensory inputs.

The role of feedback connections in shaping the responses of visual cortical neurons.

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1471-2202-9-s1-p98

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scholarly article

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10.1186/1471-2202-9-98

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9

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Cyril R. Pernet

Guillaume A. Rousselet

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2008-10-03T00:00:00Z

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26521192

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2573889

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