  Current studies about motor imagery based rehabilitation training systems for
stroke subjects lack an appropriate analytic method, which can achieve a
considerable classification accuracy, at the same time detects gradual changes
of imagery patterns during rehabilitation process and disinters potential
mechanisms about motor function recovery. In this study, we propose an adaptive
boosting algorithm based on the cortex plasticity and spectral band shifts.
This approach models the usually predetermined spatial-spectral configurations
in EEG study into variable preconditions, and introduces a new heuristic of
stochastic gradient boost for training base learners under these preconditions.
We compare our proposed algorithm with commonly used methods on datasets
collected from 2 months' clinical experiments. The simulation results
demonstrate the effectiveness of the method in detecting the variations of
stroke patients' EEG patterns. By chronologically reorganizing the weight
parameters of the learned additive model, we verify the spatial compensatory
mechanism on impaired cortex and detect the changes of accentuation bands in
spectral domain, which may contribute important prior knowledge for
rehabilitation practice.
