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Brain Computer Interface (BCI) gives opportunities to control a computer or a machine by imagination of limb movement, which activates somatosensory motor region in a discriminative manner. As far as it has been concerned, it has been not well investigated how much the given (extracted) features in BCI are discriminative in the sense of information theory. For this purpose, we cast the feature spaces corresponding to given conditions into probability spaces by yielding corresponding probability distributions. Then the relative entropy (measures to estimate the difference between two probability distributions) is introduced to measure the distance between these probability distributions. Such a distance represents well how two feature spaces are separable. We compare this distance with BCI performance (classification success rate) to see their correlation.

How Much Features in Brain-Computer Interface Are Discriminative?– Quantitative Measure by Relative Entropy