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Status: Bibliographieeintrag
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Verfasst von:Herff, Christian [VerfasserIn]   i
 Putze, Felix [VerfasserIn]   i
 Heger, Dominic [VerfasserIn]   i
 Schultz, Tanja [VerfasserIn]   i
Titel:Speaking mode recognition from functional Near Infrared Spectroscopy
Verf.angabe:Christian Herff, Felix Putze, Dominic Heger, Cuntai Guan and Tanja Schultz
E-Jahr:2012
Jahr:12 November 2012
Umfang:4 S.
Fussnoten:Gesehen am 30.08.2018
Titel Quelle:Enthalten in: Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012
Ort Quelle:Piscataway, NJ : IEEE, 2012
Jahr Quelle:2012
Band/Heft Quelle:(2012), Seite 1715-1718
ISBN Quelle:978-1-4577-1787-1
 1-4244-4119-6
 978-1-4244-4119-8
Abstract:Speech is our most natural form of communication and even though functional Near Infrared Spectroscopy (fNIRS) is an increasingly popular modality for Brain Computer Interfaces (BCIs), there are, to the best of our knowledge, no previous studies on speech related tasks in fNIRS-based BCI. We conducted experiments on 5 subjects producing audible, silently uttered and imagined speech or do not produce any speech. For each of these speaking modes, we recorded fNIRS signals from the subjects performing these tasks and distinguish segments containing speech from those not containing speech, solely based on the fNIRS signals. Accuracies between 69% and 88% were achieved using support vector machines and a Mutual Information based Best Individual Feature approach. We are also able to discriminate the three speaking modes with 61% classification accuracy. We thereby demonstrate that speech is a very promising paradigm for fNIRS based BCI, as classification accuracies compare very favorably to those achieved in motor imagery BCIs with fNIRS.
DOI:doi:10.1109/EMBC.2012.6346279
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

DOI: https://doi.org/10.1109/EMBC.2012.6346279
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Accuracy
 Adult
 audible speech
 best individual feature approach
 Brain
 brain computer interface
 brain-computer interfaces
 Corpus Callosum
 Electrodes
 Feature extraction
 functional near infrared spectroscopy
 Hemodynamics
 Humans
 imagined speech
 infrared spectra
 Male
 Motor Cortex
 mutual information
 Mutual information
 silently uttered speech
 speaking mode recognition
 Spectroscopy
 Spectroscopy, Near-Infrared
 Speech
 speech recognition
 support vector machine
 support vector machines
K10plus-PPN:1580559239
Verknüpfungen:→ Sammelwerk

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