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|Title:||Neural networks training based on sequential extended Kalman filtering for single trial EEG classification||Authors:||Turnip, A.
Sequential extended Kalman filtering
|Issue Date:||2010||Citation:||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|
|Appears in Collections:||Staff Publications|
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