  The performance of neural decoders can degrade over time due to
nonstationarities in the relationship between neuronal activity and behavior.
In this case, brain-machine interfaces (BMI) require adaptation of their
decoders to maintain high performance across time. One way to achieve this is
by use of periodical calibration phases, during which the BMI system (or an
external human demonstrator) instructs the user to perform certain movements or
behaviors. This approach has two disadvantages: (i) calibration phases
interrupt the autonomous operation of the BMI and (ii) between two calibration
phases the BMI performance might not be stable but continuously decrease. A
better alternative would be that the BMI decoder is able to continuously adapt
in an unsupervised manner during autonomous BMI operation, i.e. without knowing
the movement intentions of the user.
  In the present article, we present an efficient method for such unsupervised
training of BMI systems for continuous movement control. The proposed method
utilizes a cost function derived from neuronal recordings, which guides a
learning algorithm to evaluate the decoding parameters. We verify the
performance of our adaptive method by simulating a BMI user with an optimal
feedback control model and its interaction with our adaptive BMI decoder. The
simulation results show that the cost function and the algorithm yield fast and
precise trajectories towards targets at random orientations on a 2-dimensional
computer screen. For initially unknown and non-stationary tuning parameters,
our unsupervised method is still able to generate precise trajectories and to
keep its performance stable in the long term. The algorithm can optionally work
also with neuronal error signals instead or in conjunction with the proposed
unsupervised adaptation.
