Conference icon

Conference

Adaptive Classification by Hybrid EKF with Truncated Filtering: Brain Computer Interfacing

Abstract:
This paper proposes a robust algorithm for adaptive modelling of EEG signal classification using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis functions (RBF) and Autoregressive (AR) modeling and obtains better classification performance by truncating the filtering distribution when new observations are very informative. © 2008 Springer Berlin Heidelberg.
Publication status:
Published

Actions


Access Document


Authors


More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Volume:
5326
Pages:
370-377
Publication date:
2008
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
URN:
uuid:318f2595-c0aa-4d96-8718-e4a051e2fa99
Source identifiers:
63640
Local pid:
pubs:63640
ISBN:
978-3-540-88905-2

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP