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Anne K. Porbadnigk

Researcher at Technical University of Berlin

Publications -  18
Citations -  466

Anne K. Porbadnigk is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Noise & Electroencephalography. The author has an hindex of 13, co-authored 18 publications receiving 422 citations. Previous affiliations of Anne K. Porbadnigk include Deutsche Forschungsgemeinschaft.

Papers
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Journal ArticleDOI

Analyzing Speech Quality Perception Using Electroencephalography

TL;DR: It is shown that a certain EEG technique, event-related-potentials (ERP) analysis, is a useful and valid tool in quality research and can be monitored in conscious and presumably non-conscious stages of processing.
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EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs).

TL;DR: The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component.
Proceedings ArticleDOI

Using ERPs for assessing the (sub) conscious perception of noise

TL;DR: This paper investigates the use of event-related potentials (ERPs) as a quantitative measure for quality assessment of disturbed audio signals and shows for two subjects that a classifier based on shrinkage LDA can be applied successfully to single out stimuli, for which the noise was presumably processed subconsciously.
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The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis

TL;DR: The LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio and is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.
Journal ArticleDOI

Single-trial analysis of the neural correlates of speech quality perception

TL;DR: A novel classification approach helps to detect trials with presumably non-conscious processing at the threshold of perception and uncovers a non-trivial confounder between neural hits and neural misses.