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 | 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 |
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.