Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression.

Objective: Due to low spatial resolution and poor signal-to-noise ratio of electroencephalogram (EEG), high accuracy classifications still suffer from lots of obstacles in the context of motor imagery (MI)-based brain-machine interface (BMI) systems. Particularly, it is extremely challenging to decode multiclass MI EEG from the same upper limb. This research proposes a novel feature learning approach to address the classification problem of 6-class MI tasks, including imaginary elbow flexion/extension, wrist supination/pronation, and hand close/open within the unilateral upper limb.

An Fe-N co-doped tube-in-tube carbon nanostructure used as an efficient catalyst for the electrochemical oxygen reduction reaction.

An Fe-N co-doped tube-in-tube carbon nanostructure is synthesized for an efficient oxygen reduction reaction. Thanks to its hollow nature, the mesoporous structure is enriched while defects are not prominent, allowing excellent activity (E  = 0.851 V) and durability together with methanol tolerance in an electrochemistry test under alkaline conditions.