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Current studies about motor imagery based rehabilitation training systems forstroke subjects lack an appropriate analytic method, which can achieve aconsiderable classification accuracy, at the same time detects gradual changesof imagery patterns during rehabilitation process and disinters potentialmechanisms about motor function recovery. In this study, we propose an adaptiveboosting algorithm based on the cortex plasticity and spectral band shifts.This approach models the usually predetermined spatial-spectral configurationsin EEG study into variable preconditions, and introduces a new heuristic ofstochastic gradient boost for training base learners under these preconditions.We compare our proposed algorithm with commonly used methods on datasetscollected from 2 months' clinical experiments. The simulation resultsdemonstrate the effectiveness of the method in detecting the variations ofstroke patients' EEG patterns. By chronologically reorganizing the weightparameters of the learned additive model, we verify the spatial compensatorymechanism on impaired cortex and detect the changes of accentuation bands inspectral domain, which may contribute important prior knowledge forrehabilitation practice.

Spatial-Spectral Boosting Analysis for Stroke Patients' Motor Imagery EEG in Rehabilitation Training