  This work presents an innovative application of the well-known concept of
cortico-muscular coherence for the classification of various motor tasks, i.e.,
grasps of different kinds of objects. Our approach can classify objects with
different weights (motor-related features) and different surface frictions
(haptics-related features) with high accuracy (over 0:8). The outcomes
presented here provide information about the synchronization existing between
the brain and the muscles during specific activities; thus, this may represent
a new effective way to perform activity recognition.
