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The non-stationary nature of electroencephalography (EEG) signals makes anEEG-based brain-computer interface (BCI) a dynamic system, thus improving itsperformance is a challenging task. In addition, it is well-known that due tonon-stationarity based covariate shifts, the input data distributions ofEEG-based BCI systems change during inter- and intra-session transitions, whichposes great difficulty for developments of online adaptive data-driven systems.Ensemble learning approaches have been used previously to tackle thischallenge. However, passive scheme based implementation leads to poorefficiency while increasing high computational cost. This paper presents anovel integration of covariate shift estimation and unsupervised adaptiveensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI)related EEG classification. The proposed method first employs an exponentiallyweighted moving average model to detect the covariate shifts in the commonspatial pattern features extracted from MI related brain responses. Then, aclassifier ensemble was created and updated over time to account for changes instreaming input data distribution wherein new classifiers are added to theensemble in accordance with estimated shifts. Furthermore, using two publiclyavailable BCI-related EEG datasets, the proposed method was extensivelycompared with the state-of-the-art single-classifier based passive scheme,single-classifier based active scheme and ensemble based passive schemes. Theexperimental results show that the proposed active scheme based ensemblelearning algorithm significantly enhances the BCI performance in MIclassifications.

Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface