Abstract
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.
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Fang, C., Li, H., Ma, L. (2013). EEG Signal Classification Using the Event-Related Coherence and Genetic Algorithm. In: Liu, D., Alippi, C., Zhao, D., Hussain, A. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2013. Lecture Notes in Computer Science(), vol 7888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38786-9_11
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DOI: https://doi.org/10.1007/978-3-642-38786-9_11
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