Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI
Data(s) |
01/01/2015
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Resumo |
Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30082505/hettiarachchi-multivariatead-evid1-2015.pdf http://dro.deakin.edu.au/eserv/DU:30082505/hettiarachchi-multivariatead-evid2-2015.pdf http://dro.deakin.edu.au/eserv/DU:30082505/hettiarachchi-multivariateadaptive-2015.pdf http://www.dx.doi.org/10.1109/SMC.2015.549 |
Direitos |
2015, IEEE |
Palavras-Chave | #Science & Technology #Technology #Computer Science, Cybernetics #Computer Science, Information Systems #Computer Science, Theory & Methods #Computer Science #Brain Computer Interface #Motor Imagery #Adaptive Kalman filter #Multivariate Autoregressive Modeling #CLASSIFICATION #EIGENMODES #PARAMETERS |
Tipo |
Conference Paper |