Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9112
Main Title: Enhanced Classification Methods for the Depth of Cognitive Processing Depicted in Neural Signals
Author(s): Nicolae, Irina-Emilia
Acqualagna, Laura
Neagu, Georgeta-Mihaela
Strungaru, Rodica
Blankertz, Benjamin
Type: Article
Language Code: en
Abstract: Analyzing brain states is a difficult problem due to high variability between subjects and trials, therefore improved techniques are requested to be developed for a better discrimination between the neural components. This paper investigates multiple enhanced classification methods for neurological feature selection and discrimination of the depth of cognitive processing. The aim is to detect the strengths and weaknesses of different classification methods and benefit from their highest performances, so that the neural information could optimally be detected. As a result, we obtained a classification rate improved by at least 5% by integrating complementary information that better describe the neural activity.
URI: https://depositonce.tu-berlin.de/handle/11303/10123
http://dx.doi.org/10.14279/depositonce-9112
Issue Date: 2018
Date Available: 16-Oct-2019
DDC Class: 610 Medizin und Gesundheit
004 Datenverarbeitung; Informatik
Subject(s): electroencephalography
EEG
signal processing
classification
cognitive processing
Sponsor/Funder: EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSee
BMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, Zentrum
License: http://rightsstatements.org/vocab/InC/1.0/
Journal Title: Scientific Bulletin: Series C, Electrical Engineering and Computer Science
Publisher: University Politehnica of Bucharest
Publisher Place: Bucharest
Volume: 80
Issue: 1
EISSN: 2286-3559
ISSN: 2286-3540
Appears in Collections:FG Neurotechnologie » Publications

Files in This Item:
File Description SizeFormat 
full42e_488208.pdf396.71 kBAdobe PDFThumbnail
View/Open


Items in DepositOnce are protected by copyright, with all rights reserved, unless otherwise indicated.