Determining mental state from EEG signals using parallel implementations of neural networks
Scientific Programming - On applications analysis
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
Genetic Algorithm Based Semi-feature Selection Method
IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
Forward semi-supervised feature selection
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires an accurate classification and recognition of multichannel EEG. The design of EEG representations 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. This paper proposes a Genetic algorithm (GA) and Support Vector Machine (SVM) hybrid approach to accomplish this EEG classification task for potential BCI applications. An Oddball stimulus program and evoked event-related coherence program were designed to evaluate our method. The present study systematically evaluates the performance of the one channel pair event-related coherence feature set for EEG signal classification of auditory task. A GA approach for feature selection is presented which used to reduce the dimension of event-related coherence feature parameters. With the base classifiers of SVM, classification experiments are carried out upon real EEG recordings. Experimental results suggest the feasibilities of the new feature set, and we also derive some valuable conclusions on the performance of the EEG signal classification methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for achieving real-time BCI system based on evoked potential event-related coherence in the future.