Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/69488
Title: Backpropagation neural networks training for single trial EEG classification
Authors: Turnip, A.
Hong, K.-S.
Ge, S.S. 
Keywords: Backpropagation neural networks
Brain computer interface
Classification accuracy
EEG
Transfer rate
Issue Date: 2010
Source: Turnip, A.,Hong, K.-S.,Ge, S.S. (2010). Backpropagation neural networks training for single trial EEG classification. Proceedings of the 29th Chinese Control Conference, CCC'10 : 2462-2467. ScholarBank@NUS Repository.
Abstract: EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable signal variations due to artifacts or recognizer-subject feedback. A number of techniques recently have been developed to address the related problem of recognizer robustness to uncontrollable signal variation. In this paper, we propose a classification method entailing time-series EEG signals with backpropagation neural networks (BPNN). To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA).
Source Title: Proceedings of the 29th Chinese Control Conference, CCC'10
URI: http://scholarbank.nus.edu.sg/handle/10635/69488
ISBN: 9787894631046
Appears in Collections:Staff Publications

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