  The non-stationary nature of electroencephalography (EEG) signals makes an
EEG-based brain-computer interface (BCI) a dynamic system, thus improving its
performance is a challenging task. In addition, it is well-known that due to
non-stationarity based covariate shifts, the input data distributions of
EEG-based BCI systems change during inter- and intra-session transitions, which
poses great difficulty for developments of online adaptive data-driven systems.
Ensemble learning approaches have been used previously to tackle this
challenge. However, passive scheme based implementation leads to poor
efficiency while increasing high computational cost. This paper presents a
novel integration of covariate shift estimation and unsupervised adaptive
ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI)
related EEG classification. The proposed method first employs an exponentially
weighted moving average model to detect the covariate shifts in the common
spatial pattern features extracted from MI related brain responses. Then, a
classifier ensemble was created and updated over time to account for changes in
streaming input data distribution wherein new classifiers are added to the
ensemble in accordance with estimated shifts. Furthermore, using two publicly
available BCI-related EEG datasets, the proposed method was extensively
compared with the state-of-the-art single-classifier based passive scheme,
single-classifier based active scheme and ensemble based passive schemes. The
experimental results show that the proposed active scheme based ensemble
learning algorithm significantly enhances the BCI performance in MI
classifications.
