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.
|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:
|OA Chapter - Parametric Modelling of EEG Data for the Identification of Mental Tasks-2-21.pdf||520.6 kB||Adobe PDF||View/Open|
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.