A Proposal of Boosting Algorithm by Probabilistic Data Interpolation for Brain-Computer Interface

Abstract

Brain-computer interface(BCI) and brain-machine interface(BMI) have been come into the research limelight. The outer computer and machine are controlled by brain activity and the discriminated boundary, which are detected with near-infrared spectroscopy(NIRS) or electroencephalograph(EEG). In this paper, we propose a new boosting algorithm for BCI using probabilistic data interpolation. In our model, interpolated data are generated by probabilistic distribution and assorted around errors instead of weights in the conventional Adaboost. By the interpolated data, the discriminated boundary is identified to control the outer machine effectively.