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Adaptive classification by hybrid EKF with truncated filtering: Brain computer interfacing

Yoon, JW and Roberts, SJ and Dyson, M and Gan, JQ (2008) Adaptive classification by hybrid EKF with truncated filtering: Brain computer interfacing. In: UNSPECIFIED, ? - ?.

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 15 Dec 2012 16:24
Last Modified: 06 Feb 2019 00:15
URI: http://repository.essex.ac.uk/id/eprint/4186

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