  This paper deals with the detection of mu-suppression from
electroencephalographic (EEG) signals in brain-computer interface (BCI). For
this purpose, an efficient algorithm is proposed based on a statistical model
and a linear classifier. Precisely, the generalized extreme value distribution
(GEV) is proposed to represent the power spectrum density of the EEG signal in
the central motor cortex. The associated three parameters are estimated using
the maximum likelihood method. Based on these parameters, a simple and
efficient linear classifier was designed to classify three types of events:
imagery, movement, and resting. Preliminary results show that the proposed
statistical model can be used in order to detect precisely the mu-suppression
and distinguish different EEG events, with very good classification accuracy.
