Gaussian mixture modeling in stroke patients´ rehabilitation EEG data analysis

Traditional 2-class Motor Imagery (MI) Electroencephalography (EEG) classification approaches like Common Spatial Pattern (CSP) and Support Vector Machine (SVM) usually underperform when processing stroke patients´ rehabilitation EEG which are flooded with unknown irregular patterns. In this paper, the classical CSP-SVM schema is improved and a feature learning method based on Gaussian Mixture Model (GMM) is utilized for depicting patients´ imagery EEG distribution features. We apply the proposed modeling program in two different modules of our online BCI-FES rehabilitation platform and achieve a relatively higher discrimination accuracy. Sufficient observations and test cases on patients´ MI data sets have been implemented for validating the GMM model. The results also reveal some working mechanisms and recovery appearances of impaired cortex during the rehabilitation training period.