Please use this identifier to cite or link to this item:
Title: Parametric modelling of EEG data for the identification of mental tasks
Authors: Camilleri, Kenneth P.
Cassar, Tracey A.
Fabri, Simon G.
Keywords: Electroencephalography
Parameter estimation
Biomedical engineering
Issue Date: 2011
Publisher: InTech
Citation: Camilleri, K. P., Cassar, T. A., & Fabri, S. G. (2011). Parametric modelling of EEG data for the identification of mental tasks. In Laskovski, A. N. (ed.), Biomedical engineering, trends in electronics, communications and software. Rijeka: InTech. 367-386.
Abstract: Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which makes the processing of such signals for the extraction of useful information a challenging task.
Appears in Collections:Scholarly Works - FacEngSCE

Files in This Item:
File Description SizeFormat 
OA Chapter - Parametric Modelling of EEG Data for the Identification of Mental Tasks-2-21.pdf520.6 kBAdobe PDFView/Open

Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.