An adaptive ensemble model for brain-computer interfaces

Brain-computer interface (BCI) have recently entered the research limelight. In many such systems, external computers and machines are controlled by brain activity signals measured using near-infrared spectroscopy (NIRS) or electroencephalograph (EEG) devices. In this paper, we propose a probabilistic data interpolation-boosting algorithm for BCI, where we adopt three evaluation criterions to decide the class of interpolated data around the misclassified data. By using the interpolated data with classes, the discriminated boundary is shown to control the external machine effectively. We verify our boosting method with numerical examples, and discuss the results.