Classification of self-paced finger movements with EEG signals using neural network and evolutionary approaches

The dependable operation of brain-computer interfaces (BCI) based on 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.