A new algorithm for EEG feature selection using mutual information
An EEG feature selection technique for the purpose of classification is developed. The technique selects those features that have maximum mutual information with the specified classes of interest (two classes in this case). Obviously, the simplest way is to consider all possible feature subsets (M out of N). However, even with a small number of features, this procedure is computationally impossible and can not be used in practice. Given the fact that most features used to represent the EEG signal are sets of features (such as AR parameters), our technique considers a trade off between computational cost and chosen feature combination. This contrasts other techniques which select features individually. The classification accuracy of features obtained by applying our technique outperforms those obtained by applying individual feature selection methods when applied to EEG signals.