@inbook {622,
	title = {Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair},
	booktitle = {Biomedical Engineering Systems and Technologies},
	series = {Communications in Computer and Information Science},
	volume = {52},
	year = {2010},
	month = {03/2010},
	pages = {64-73},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	edition = {Ana Fred, Joaquim Filipe and Hugo Gamboa},
	chapter = {Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair},
	address = {Berlin},
	abstract = {Two distinct signal features suitable to be used as input to a Support-Vector Machine (SVM) classifier in an application involving hands motor imagery and the correspondent EEG signal are evaluated in this paper. Such features are the Power Spectral Density (PSD) components and the Adaptive Autoregressive (AAR) parameters. The best result (an accuracy of 97.1\%) is obtained when using PSD components, while the AAR parameters generated an accuracy of 91.4\%. The results also demonstrate that it is possible to use only two EEG channels (bipolar configuration around C_3 and C_4), discarding the bipolar configuration around C_z. The algorithms were tested with a proprietary EEG data set involving 4 individuals and with a data set provided by the University of Graz (Austria) as well. The resulting classification system is now being implemented in a Brain-Computer Interface (BCI) used to guide a robotic wheelchair.},
	keywords = {Brain-Computer Interfaces, Power Spectral Density components, RoboticWheelchair., Support-Vector Machines},
	isbn = {978-3-642-11720-6 (Print), 978-3-642-11721-3 (Online)},
	issn = {1865-0929 (Print), 1865-0937 (Online)},
	doi = {10.1007/978-3-642-11721-3},
	url = {http://www.springerlink.com/content/wx411876025n1613/},
	author = {Andre Ferreira and Teodiano Freire and Mario Sarcinelli and Jose Luis Martin and Garcia, Juan Carlos and Manuel Mazo}
}
