Abstract
Brain-computer interface (BCI) and brain-machine interface (BMI) technologies 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 new boosting algorithm for BCI using a possibilistic data interpolation scheme. In our model, interpolated data is generated around classification errors using membership function, and the class attribute is decided by a rule with three kinds of criterions. By using the interpolated data, the discriminated boundary is shown to control the external machine effectively. We verify our boosting method with some numerical examples in which NIRS data is assumed to detect from subjects, and discuss the results.
