Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Hettiarachchi, Imali T., Nguyen, Thanh Thi and Nahavandi, Saeid 2015, Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI, in SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 3164-3168, doi: 10.1109/SMC.2015.549.

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Title Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI
Author(s) Hettiarachchi, Imali T.ORCID iD for Hettiarachchi, Imali T. orcid.org/0000-0002-4220-0970
Nguyen, Thanh ThiORCID iD for Nguyen, Thanh Thi orcid.org/0000-0001-9709-1663
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)
Conference location Hong Kong, China
Conference dates 9-12 Oct. 2015
Title of proceedings SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics
Publication date 2015
Series Systems Man and Cybernetics Conference Proceedings
Start page 3164
End page 3168
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) 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
Summary 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.
ISSN 1062-922X
Language eng
DOI 10.1109/SMC.2015.549
Field of Research 090609 Signal Processing
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082505

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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