Dynamically weighted ensemble classification for non-stationary EEG processing

The non-stationary nature of EEG poses a major challenge to robust operation of brain-computer interfaces (BCIs). The objective of this paper is to propose and investigate a computational method to address non-stationarity in EEG classification.

We developed a novel dynamically weighted ensemble classification (DWEC) framework whereby an ensemble of multiple classifiers are trained on clustered features. The decisions from these multiple classifiers are dynamically combined based on the distances of the cluster centres to each test data sample being classified.

The clusters of the feature space from the second session spanned a different space compared to the clusters of the feature space from the first session which highlights the processes of session-to-session non-stationarity. The session-to-session performance of the proposed DWEC method was evaluated on two datasets. The results on publicly available BCI Competition IV dataset 2A yielded a significantly higher mean accuracy of 81.48% compared to 75.9% from the baseline support vector machine (SVM) classifier without dynamic weighting. Results on the data collected from our twelve in-house subjects yielded a significantly higher mean accuracy of 73% compared to 69.4% from the baseline SVM classifier without dynamic weighting.

The cluster based analysis provides insight into session-to-session non-stationarity in EEG data. The results demonstrate the effectiveness of the proposed method in addressing non-stationarity in EEG data for the operation of a BCI.