Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Konferenzbeitrag

Introducing the Detection of Auditory Error Responses Based on BCI Technology for Passive Interaction

MPG-Autoren
/persons/resource/persons84325

Zander,  TO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

http://lamp.tugraz.at/~karl/verlagspdf/Proceeding_BCI_Conference_2011_neu.pdf
(beliebiger Volltext)

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Zander, T., Klippel, D., & Scherer, R. (2011). Introducing the Detection of Auditory Error Responses Based on BCI Technology for Passive Interaction. In G. Müller-Putz, R. Scherer, M. Billinger, A. Kreilinger, V. Kaiser, & C. Neuper (Eds.), 5th International Brain-Computer Interface Conference 2011 (pp. 252-255). Graz, Austria: Verlag der Technischen Universität Graz.


Zitierlink: https://hdl.handle.net/21.11116/0000-0001-F2E1-F
Zusammenfassung
In this article we will introduce a passive BCI system for detecting responses of the human brain on the perception of errors in music. We played cadences, sequences of chords, to subjects who are experts in music theory and actively play an classical instrument. As the expectancy is highest at the ending chord, we randomly introduced cadences with erroneous ending. In consistence with previous studies from the neurosciences we evoked an event-related potential, mainly consisting of an early right anterior negativity re ecting syntatcic error processing followed by a stronger negativity in erroneous trials at 500 ms, induced by semantic processing. We could identify single trials of these processes with a standardized, crossvalidated offline classification scheme, resulting in an accuracy of 75.7 %.