Energy feature extraction of EEG signals and a case study

Energy is very important in electroencephalogram (EEG) signal classification. In this paper, a criterion called extreme energy difference (EED) is devised, which is a discriminative objective function to guide the process of spatially filtering EEG signals. The energy of the filtered EEG signals has the optimal discriminative capability under the EED criterion, and therefore EED can be considered as a feature extractor. The solution which optimizes the EED criterion is presented in this paper and according to experimental results, EED is a promising method for extracting energy features in EEG signal classification.