  EEG-BCIs have been well studied in the past decades and implemented into
several famous applications, like P300 speller and wheelchair controller.
However, these interfaces are indirect due to low spatial resolution of EEG.
Recently, direct ECoG-BCIs attract intensive attention because ECoG provides a
higher spatial resolution and signal quality. This makes possible localization
of the source of neural signals with respect to certain brain functions. In
this article, we present a realization of ECoG-BCIs for finger flexion
prediction provided by BCI competition IV. Methods for finger flexion
prediction including feature extraction and selection are provided in this
article. Results show that the predicted finger movement is highly correlated
with the true movement when we use band-specific amplitude modulation.
