Please use this identifier to cite or link to this item: https://doi.org/10.1109/KSE.2010.42
Title: Neural networks training based on sequential extended Kalman filtering for single trial EEG classification
Authors: Turnip, A.
Hong, K.-S.
Ge, S.S. 
Jeong, M.Y.
Keywords: Accuracy
Classification
Electroencephalography
Neural networks
Sequential extended Kalman filtering
Transfer rate
Issue Date: 2010
Source: Turnip, A.,Hong, K.-S.,Ge, S.S.,Jeong, M.Y. (2010). Neural networks training based on sequential extended Kalman filtering for single trial EEG classification. Proceedings - 2nd International Conference on Knowledge and Systems Engineering, KSE 2010 : 85-88. ScholarBank@NUS Repository. https://doi.org/10.1109/KSE.2010.42
Abstract: The nonstationary nature of the brain signals provides a rather unstable input resulting in uncertainty and complexity in the control. Intelligent processing algorithms adapted to the task are a prerequisite for reliable BCI applications. This work presents a novel intelligent processing strategy for the realization of an effective BCI which has the capability to improved classification accuracy and communication rate as well. A neural networks training based on sequential extended Kalman filtering analysis for classification of extracted EEG signal is proposed. A statistically significant improvement was achieved with respect to the rates provided by raw data. © 2010 IEEE.
Source Title: Proceedings - 2nd International Conference on Knowledge and Systems Engineering, KSE 2010
URI: http://scholarbank.nus.edu.sg/handle/10635/84005
ISBN: 9780769542133
DOI: 10.1109/KSE.2010.42
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