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Neural Networks Training Based on Sequential Extended Kalman Filtering for Single Trial EEG Classification

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4 Author(s)
Arjon Turnip ; Dept. of Cogno Mechatron. Eng., Pusan Nat. Univ., Busan, South Korea ; Keum-Shik Hong ; Shuzhi Sam Ge ; Myung Yung Jeong

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.

Published in:

Knowledge and Systems Engineering (KSE), 2010 Second International Conference on

Date of Conference:

7-9 Oct. 2010