René Pallenberg, M.Sc.

Photo of René  Pallenberg, M.Sc.

Research Associate


Institute for Signal Processing
University of Luebeck
Ratzeburger Allee 160
23562 Lübeck

Gebäude 64, 1. OG, Raum 92

Email: r.pallenberg(at)uni-luebeck.de
Phone: +49 451 3101 5820

Research Focus/ Forschungsschwerpunkt

Auditory Attention Detection:

Investigates the detection of a subject's attentional focus during listening with the help of EEG. The EEG signal is used to predict which speaker a subject is currently listening to or in which direction their audio focus lies. This could be used to improve hearing aids in the future. Within this research topic, both EEG and audio data are processed. In addition to classical signal processing and pattern recognition methods such as filter banks and SVMs, current artificial neural networks are also used.

Erkennung der auditiven Aufmerksamkeit:

Untersucht die Erkennung des Aufmerksamkeitsfokus einer Person während des Zuhörens mithilfe eines EEGs. Das EEG-Signal wird verwendet, um vorherzusagen, welchem Sprecher ein Proband gerade zuhört oder in welche Richtung sein Audio-Fokus liegt. Dies könnte in Zukunft zur Verbesserung von Hörgeräten genutzt werden. In diesem Forschungsthema werden sowohl EEG- als auch Audiodaten verarbeitet. Neben klassischen Signalverarbeitungs- und Mustererkennungsmethoden wie Filterbanken und SVMs kommen auch aktuelle künstliche neuronale Netze zum Einsatz.

 

 

Publications

2020

  • René Pallenberg, Marja Fleitmann, Kira Soika, Andreas Martin Stroth, Jan Gerlach, Alexander Fürschke, Jörg Barkhausen, Arpad Bischof, and Heinz Handels,
    Automatic quality measurement of aortic contrast-enhanced CT angiographies for patient-specific dose optimization, vol. 15, no. 10, pp. 1611--1617, 2020.
    DOI:10.1007/s11548-020-02238-4
    File: Pallenberg2020_Article_AutomaticQualityMeasurementOfA.pdf
    Bibtex: BibTeX
    @article{pallenberg_automatic_2020,
    	title = {Automatic quality measurement of aortic contrast-enhanced CT angiographies for patient-specific dose optimization},
    	volume = {15},
    	issn = {1861-6429},
    	url = {https://doi.org/10.1007/s11548-020-02238-4},
    	doi = {10.1007/s11548-020-02238-4},
    	abstract = {Iodine-containing contrast agent ({CA}) used in contrast-enhanced {CT} angiography ({CTA}) can pose a health risk for patients. A system that adjusts the frequently used standard {CA} dose for individual patients based on their clinical parameters can be useful. As basis the quality of the image contrast in {CTA} volumes has to be determined, especially to recognize excessive contrast induced by {CA} overdosing. However, a manual assessment with a {ROI}-based image contrast classification is a time-consuming step in everyday clinical practice.},
    	pages = {1611--1617},
    	number = {10},
    	journaltitle = {International Journal of Computer Assisted Radiology and Surgery},
    	shortjournal = {Int J {CARS}},
    	author = {Pallenberg, René and Fleitmann, Marja and Soika, Kira and Stroth, Andreas Martin and Gerlach, Jan and Fürschke, Alexander and Barkhausen, Jörg and Bischof, Arpad and Handels, Heinz},
    	urldate = {2021-03-22},
    	year = {2020},
            month = {October},
    	langid = {english},
    	file = {Springer Full Text PDF:C\:\\Users\\René\\Zotero\\storage\\MRNGAKY6\\Pallenberg et al. - 2020 - Automatic quality measurement of aortic contrast-e.pdf:application/pdf},
    }