about
Large-scale cortical correlation structure of spontaneous oscillatory activityMotor cortex activity predicts response alternation during sensorimotor decisionsAccounting for linear transformations of EEG and MEG data in source analysis.Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach.Single-trial EEG-fMRI reveals the dynamics of cognitive function.Oscillatory synchronization in large-scale cortical networks predicts perception.Cortical hypersynchrony predicts breakdown of sensory processing during loss of consciousness.Spectral fingerprints of large-scale neuronal interactions.Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention.Buildup of choice-predictive activity in human motor cortex during perceptual decision making.Neural substrates of cognitive capacity limitations.Stronger Neural Modulation by Visual Motion Intensity in Autism Spectrum DisordersCortical information flow during flexible sensorimotor decisions.The Tactile Window to Consciousness is Characterized by Frequency-Specific Integration and Segregation of the Primary Somatosensory Cortex.Dissociating neuronal gamma-band activity from cranial and ocular muscle activity in EEGPhase-dependent neuronal coding of objects in short-term memory.Cortical network dynamics of perceptual decision-making in the human brainGamma-band activity reflects attentional guidance by facial expression.Physiological processes non-linearly affect electrophysiological recordings during transcranial electric stimulation.Corticostriatal coordination through coherent phase-amplitude coupling.Motor actions influence subsequent sensorimotor decisions.Altered intrinsic neuronal interactions in the visual cortex of the blind.Population activity in the human dorsal pathway predicts the accuracy of visual motion detection.BOLD fMRI Correlation Reflects Frequency-Specific Neuronal Correlation.High-frequency activity in human visual cortex is modulated by visual motion strength.Identification of sensory blockade by somatosensory and pain-induced evoked potentials.Right temporoparietal gray matter predicts accuracy of social perception in the autism spectrum.Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG.A framework for local cortical oscillation patternsIntegrating top-down and bottom-up sensory processing by somato-dendritic interactionsTowards single-trial analysis in cognitive brain researchTrial-by-Trial Coupling of Concurrent Electroencephalogram and Functional Magnetic Resonance Imaging Identifies the Dynamics of Performance MonitoringMonkey EEG links neuronal color and motion information across species and scalesInvestigating large-scale brain dynamics using field potential recordings: analysis and interpretation
P50
Q24630674-DCDF4B6F-431A-4EEC-942A-D5CC77F578E4Q30821101-A9A852CD-89D7-451B-946E-79693D496B74Q30922378-0DFE73A3-B2A0-46B0-82FF-CFCF7ACD6AF1Q31013662-EA63AA2B-16E6-454D-A189-F452967C23FAQ31072049-C6473B08-A668-4B12-A3E1-9C7F5FD224A7Q34160595-9C02EF5A-741A-4476-B00B-445581BEBF9CQ34234213-28F5AA63-1B72-4569-80DF-8FCB7BD88D09Q34246123-5EBA1B9C-65F3-4BB0-893B-A452C93AD114Q34889707-D345A624-4BB2-40CF-8D14-3DC19F0C922CQ35002374-2DD7518F-639F-44D7-8415-034D2773B8E9Q35090913-667B70F7-5C12-4673-8349-AFDEB143DAB1Q35683481-B1BAB51D-AF88-4525-88F0-8F673A112524Q36487070-D4DBB773-C394-4BD2-9D92-112EDE19C33BQ36569489-A1344F11-C9BB-48C2-BC09-B670A675F3BFQ36997724-712CEBAA-2983-43FE-A9E6-FEBFF5D614B2Q37426973-F2ACDB71-67DF-465B-922B-0A76535BEB81Q37856201-931EDD08-A26A-49DC-8933-B81781FAB089Q39380421-D18EC6DB-3AB5-45D2-AB53-7B13700338B9Q39868246-14C1A06E-64FA-40A1-9B51-A3BE708E9952Q42457906-1F06DB38-ED24-41A7-90E3-7AF9DC69F585Q45712352-C7A3662D-19BD-4BC1-A0D7-AB22D7B96FE8Q46326092-C138EA53-E7C1-41F5-8FCB-67AC69F0A982Q48172311-43BC5233-BE75-4F4D-991D-1F38204CE65BQ48196530-D1ADC027-25C8-44EB-9D80-E7AEF4BA06FFQ48561217-C6406254-F590-45ED-855D-128C693AA121Q48608186-68588BB5-00FB-495C-A28D-D768D961DF58Q48825696-54F89867-91B8-4324-9919-613A8084F028Q48939602-F48BD2B7-53F6-4C0C-AA4E-8E78EB8F069FQ56971164-88E547E7-1342-4A51-B8B6-8D237854B139Q57860980-A9B82CD4-66E5-45A5-9AA6-E64825ED3A55Q60735929-1CCB6297-D3A0-4989-900C-D03E02D4A21EQ60735951-7661AD2A-310A-48C7-8F9D-D4EF23EF6317Q83231552-745197F6-64FF-42A8-BECF-81562A619EFBQ89243163-0D67A683-A320-4854-AF2C-D22478A773E2
P50
description
German neuroscientist
@en
deutscher Neurowissenschaftler
@de
wetenschapper
@nl
հետազոտող
@hy
name
Markus Siegel
@ast
Markus Siegel
@de
Markus Siegel
@en
Markus Siegel
@es
Markus Siegel
@fr
Markus Siegel
@nl
Markus Siegel
@sl
type
label
Markus Siegel
@ast
Markus Siegel
@de
Markus Siegel
@en
Markus Siegel
@es
Markus Siegel
@fr
Markus Siegel
@nl
Markus Siegel
@sl
prefLabel
Markus Siegel
@ast
Markus Siegel
@de
Markus Siegel
@en
Markus Siegel
@es
Markus Siegel
@fr
Markus Siegel
@nl
Markus Siegel
@sl
P214
P227
P106
P21
P214
P227
P2456
P31
P3829
P496
0000-0001-5115-936X
P569
1974-01-01T00:00:00Z
P734
P735
P7859
viaf-20776958