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Investigating Visual Cognition with Electrophysiology

Investigating Visual Cognition with Electrophysiology. Berlin School of Mind and Brain, Humboldt-University Berlin Institute of Medical Psychology, Charité Berlin niko.busch@charite.de. Prof. Niko Busch Dr. Maximilien Chaumon Felix Ball Isabelle Bareither Sophie Herbst Caroline Szymanski

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Investigating Visual Cognition with Electrophysiology

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  1. Investigating Visual Cognition with Electrophysiology Berlin School of Mind and Brain, Humboldt-University Berlin Institute of Medical Psychology, Charité Berlin niko.busch@charite.de • Prof. Niko Busch • Dr. Maximilien Chaumon • Felix Ball • Isabelle Bareither • Sophie Herbst • Caroline Szymanski • Lyudmyla Kovalenko • Anne Elzemann • www.oszillab.net • Research topics: • Object recognition: • spatial frequencies • semantic context • Relationship between spontaneous EEG oscillations and perception. • Saccadic remapping and visual stability • Conscious and unconscious perception • Change blindness • Object substitution masking • Re-entrant processing • Time perception: • Duration/passage of time • temporally discrete or continuous Time-frequency analysis • Event-related oscillations • Single-trial correlation betweenspontaneous brain activityand perception • Oscillatory power and phase • Circular statistics effect of pre-stimulus alpha poweron perception and attention effect of pre-stimulus alpha phaseon ERP amplitude gamma-band power during implicit learning General linear modelling • Comprehensive, unbiased data-minig for EEG • Single-trial analyses Flexible single subject and group analysis. Robust statistical inference (multiple comparison correction) through bootstraping and clustering methods. For each electrode, each time point independently Split variance in the data between several conditions (ANOVA) or any type of continuous predictor (ANCOVA) Eliminating confounding factors in EEG responses Linear relationship between physical features of an image and EEG response Independent component analysis/automated artifact correction • ICA using EEGlab • Detect typical artifactual components and remove. Correlation with EOG electrodes("blink" component) Low autocorrelation("muscle" component) Focal component("bad electrode" component) High trial variability component("bad electrode" component) Example detected artifacts Six automated rejection methods Summary of automatically rejected components • Representative publications: • Kovalenko, Chaumon & Busch (2012): A Pool of Pairs of Related Objects (POPORO) for Investigating Visual Semantic Integration: Behavioral and Electrophysiological Validation. • Busch & VanRullen (2010): Spontaneous EEG oscillations reveal periodic sampling of visual attention. • Chaumon, Schwartz & Tallon-Baudry (2009): Unconscious learning versus visual perception: dissociable roles for gamma oscillations revealed in MEG. • Busch et al. (2006): A cross-laboratory study of event-related gamma activity in a standard object recognition paradigm.

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