Comparing EEG, its time-derivative and their joint use as features in a BCI for 2-D pointer control

Efficient and accurate classification of event related potentials is a core task in brain-computer interfaces (BCI). This is normally obtained by first extracting features from the voltage amplitudes recorded via EEG at different channels and then feeding them into a classifier. In this paper we evaluate the relative benefits of using the first order temporal derivatives of the EEG signals, not the EEG signals themselves, as inputs to the BCI: an area that has not been thoroughly examined. Specifically, we compare the classification performance of features extracted from the first derivative, with those derived from the amplitude, as well as their combination using data from a P300-based BCI mouse. Features were selected based on the absolute difference of medians of the target and non-target classes. Classification was carried out by an ensemble of linear support vector machines which were optimised using the mutual information criterion. Comparisons were based on the area under the receiver operating characteristics. The Mann-Whitney one-tailed test was used to study significance. Results show that EEG amplitudes are outperformed by both the first derivative and the combined feature vector and that derivatives are better than the combined vector.