Publication: Accounting for Heterogeneity: Mixed-Effects Models in Resting-State EEG Data in a Sample of Tinnitus Sufferers
Accounting for Heterogeneity: Mixed-Effects Models in Resting-State EEG Data in a Sample of Tinnitus Sufferers
Date
Date
Date
Citations
Riha, C., Güntensperger, D., Kleinjung, T., & Meyer, M. (2020). Accounting for Heterogeneity: Mixed-Effects Models in Resting-State EEG Data in a Sample of Tinnitus Sufferers. Brain Topography, 33(4), 413–424. https://doi.org/10.1007/s10548-020-00772-7
Abstract
Abstract
Abstract
In neuroscience, neural oscillations and other features of brain activity recorded by electroencephalography (EEG) are typically statistically assessed on the basis of the study's population mean to identify possible blueprints for healthy subjects, or subjects with diagnosable neurological or psychiatric disorders. Despite some inter-individual similarities, there is reason to believe that a discernible portion of the individual brain activity is subject-specific. In order to encompass the potential individual source of variance in E
Metrics
Downloads
Views
Additional indexing
Creators (Authors)
- Güntensperger, Dominik
- Kleinjung, Tobias
Volume
Volume
Volume
Number
Number
Number
Page range/Item number
Page range/Item number
Page range/Item number
Page end
Page end
Page end
Item Type
Item Type
Item Type
Dewey Decimal Classifikation
Dewey Decimal Classifikation
Dewey Decimal Classifikation
Keywords
Language
Language
Language
Publication date
Publication date
Publication date
Date available
Date available
Date available
ISSN or e-ISSN
ISSN or e-ISSN
ISSN or e-ISSN
OA Status
OA Status
OA Status
Free Access at
Free Access at
Free Access at
Publisher DOI
Metrics
Downloads
Views
Citations
Riha, C., Güntensperger, D., Kleinjung, T., & Meyer, M. (2020). Accounting for Heterogeneity: Mixed-Effects Models in Resting-State EEG Data in a Sample of Tinnitus Sufferers. Brain Topography, 33(4), 413–424. https://doi.org/10.1007/s10548-020-00772-7
