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Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI
conference contributionposted on 2015-01-01, 00:00 authored by Imali HettiarachchiImali Hettiarachchi, Thanh Thi NguyenThanh Thi Nguyen, Saeid Nahavandi
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.
EventIEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)
SeriesSystems Man and Cybernetics Conference Proceedings
Pagination3164 - 3168
LocationHong Kong, China
Place of publicationPiscataway, N.J.
Publication classificationE Conference publication; E1 Full written paper - refereed
Copyright notice2015, IEEE
Title of proceedingsSMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics
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