Common Spatial-Spectral Boosting Pattern for Brain-Computer Interface

Classification of multichannel electroencephalogram (EEG) recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). Frequency bands and channels configuration that relate to brain activities associated with BCI tasks are often pre-decided as default in EEG analysis without deliberations. However, a steady configuration usually loses effects due to individual variability across different subjects in practical applications. In this paper, we propose an adaptive boosting algorithm in a unifying theoretical framework to model the usually predetermined spatial-spectral configurations into variable preconditions, and further introduce a novel heuristic of stochastic gradient boost for training base learners under these preconditions. We evaluate the effectiveness and robustness of our proposed algorithm based on two data sets recorded from diverse populations including the healthy people and stroke patients. The results demonstrate its superior performance.