Please use this identifier to cite or link to this item: https://doi.org/10.2316/P.2011.723-135
Title: EEG-P300 extraction using neural network based adaptive recursive filter and adaptive autoregressive models
Authors: Turnip, A.
Hong, K.-S.
Ge, S.S. 
Keywords: Accuracy
Adaptive feature extraction
Brain computer interface
Classification
EEG-P300
Neural networks
Transfer rate
Issue Date: 2011
Source: Turnip, A.,Hong, K.-S.,Ge, S.S. (2011). EEG-P300 extraction using neural network based adaptive recursive filter and adaptive autoregressive models. Proceedings of the 8th IASTED International Conference on Biomedical Engineering, Biomed 2011 : 36-41. ScholarBank@NUS Repository. https://doi.org/10.2316/P.2011.723-135
Abstract: In this paper, an adaptive feature extraction for EEG-based P300 signals is presented by combining the adaptive recursive (AR) filter and adaptive autoregressive (AAR) model. The extracted signals are then classified using multilayer neural networks (MNNs). It was found that the application of the proposed methods are improving and strengthening the EEG signal according to the small-amplitude of the P300 component in the EEG signals. The experimental results on the EEG raw data show that the implementation of the proposed method achieves a statistically significant improvement.
Source Title: Proceedings of the 8th IASTED International Conference on Biomedical Engineering, Biomed 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/70064
ISBN: 9780889868663
DOI: 10.2316/P.2011.723-135
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