Assessing features for electroencephalographic signal categorization

The classification of electroencephalographic (EEG) signals is an important issue in the ongoing research of brain-computer interface (BCI) technology. One such BCI uses slow cortical potential measures to infer user intent from the original brain activity. Seven features based on the standard low-level signal properties are evaluated for their ability to classify brain activities, and thus make up for the scarcity of signal features for the current EEG signal categorization. In addition, a paradigm is proposed to select effective low-level features for EEG signal classification. Combining the features selected by the paradigm with the DC value of slow cortical potentials for categorization based on a Bayesian classifier, we obtained significant improvement on classification accuracy for data set Ia of the BCI competition 2003, which is a typical representative of one kind of BCI data.