File(s) under permanent embargo
Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI
conference contribution
posted on 2015-01-01, 00:00 authored by Imali HettiarachchiImali Hettiarachchi, Thanh Thi NguyenThanh Thi Nguyen, Saeid NahavandiAdaptive 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
Event
IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)Series
Systems Man and Cybernetics Conference ProceedingsPagination
3164 - 3168Publisher
IEEELocation
Hong Kong, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-10-09End date
2015-10-12ISSN
1062-922XLanguage
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and CyberneticsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC