Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCA.2009.5410152
Title: Classification of self-paced finger movements with EEG signals using neural network and evolutionary approaches
Authors: Liyanage, S.R.
Xu, J.-X. 
Guan, C.
Ang, K.K.
Zhang, C.S.
Lee, T.H. 
Issue Date: 2009
Source: Liyanage, S.R., Xu, J.-X., Guan, C., Ang, K.K., Zhang, C.S., Lee, T.H. (2009). Classification of self-paced finger movements with EEG signals using neural network and evolutionary approaches. 2009 IEEE International Conference on Control and Automation, ICCA 2009 : 1807-1812. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCA.2009.5410152
Abstract: The dependable operation of brain-computer interfaces (BCI) based on electro electroencephalogram (EEG) signals requires precise classification of multi-channel EEG signals. The design of EEG interpretation and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. In this paper we attempt to classify EEG data used in the BCI competition by the combination of pattern classification methods. We use Common Spatial Pattern (CSP) to extract features. A Genetic Algorithm (GA) was applied first to evolve an artificial neural network (ANN) to find the optimum structure of ANN. A Particle Swarm Optimization (PSO) was also attempted to determine the optimal number of hidden neurons complementary to the GA approach. Then the GA was used to evolve the connection weights of the ANN. ©2009 IEEE.
Source Title: 2009 IEEE International Conference on Control and Automation, ICCA 2009
URI: http://scholarbank.nus.edu.sg/handle/10635/69607
ISBN: 9781424447060
DOI: 10.1109/ICCA.2009.5410152
Appears in Collections:Staff Publications

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