  Internet of Things is rapidly spreading across several fields, including
healthcare, posing relevant questions related to communication capabilities,
energy efficiency and sensors unobtrusiveness. Particularly, in the context of
recognition of gestures, e.g., grasping of different objects, brain and
muscular activity could be simultaneously recorded via EEG and EMG,
respectively, and analyzed to identify the gesture that is being accomplished,
and the quality of its performance. This paper proposes a new algorithm that
aims (i) to robustly extract the most relevant features to classify different
grasping tasks, and (ii) to retain the natural meaning of the selected
features. This, in turn, gives the opportunity to simplify the recording setup
to minimize the data traffic over the communication network, including
Internet, and provide physiologically significant features for medical
interpretation. The algorithm robustness is ensured both by consensus
clustering as a feature selection strategy, and by nested cross-validation
scheme to evaluate its classification performance.
