Comparison of extended and unscented Kalman filters applied to EEG signals

Walters-Williams, Janett and Li, Yan (2010) Comparison of extended and unscented Kalman filters applied to EEG signals. In: 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010), 13-15 July 2010, Gold Coast, Australia.


For years the Extended Kalman Filter (EKF) has been the algorithm for non-linear systems due to its simplicity
and suitability to real time implementations. Because of its
shortfalls, however, the Unscented Kalman Filter (UKF) was
introduced to be an algorithm which was more accurate. Since
then researches have been conducted to investigate the
suitability of both algorithms in different areas. This paper presents a comparison of the estimation quality for the two algorithms when applied to a real time system -
Electroencephalography (EEG).

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 28 Feb 2011 07:14
Last Modified: 14 Mar 2018 02:54
Uncontrolled Keywords: extended Kalman filter; electroencephalography; EEG signals; electroencephalography; unscented Kalman filter; EEG
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/ICCME.2010.5558873

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