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Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Version 2 2024-06-05, 11:49
Version 1 2016-03-31, 14:43
conference contribution
posted on 2024-06-05, 11:49 authored by Imali HettiarachchiImali Hettiarachchi, TT Nguyen, S 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.

History

Pagination

3164-3168

Location

Hong Kong, China

Start date

2015-10-09

End date

2015-10-12

ISSN

1062-922X

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2015, IEEE

Title of proceedings

SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics

Event

IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)

Publisher

IEEE

Place of publication

Piscataway, N.J.

Series

Systems Man and Cybernetics Conference Proceedings