  While invasively recorded brain activity is known to provide detailed
information on motor commands, it is an open question at what level of detail
information about positions of body parts can be decoded from non-invasively
acquired signals. In this work it is shown that index finger positions can be
differentiated from non-invasive electroencephalographic (EEG) recordings in
healthy human subjects. Using a leave-one-subject-out cross-validation
procedure, a random forest distinguished different index finger positions on a
numerical keyboard above chance-level accuracy. Among the different spectral
features investigated, high $\beta$-power (20-30 Hz) over contralateral
sensorimotor cortex carried most information about finger position. Thus, these
findings indicate that finger position is in principle decodable from
non-invasive features of brain activity that generalize across individuals.
