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 novel boosting algorithm for BCI using a probabilistic data interpolation scheme. In our model, interpolated data is generated around classification errors using a probability distribution function, as opposed to conventional AdaBoost which increases weights corresponding to the misclassified examples. By using the interpolated data, the discriminated boundary is shown to control the external machine effectively. We verify our boosting method with an experiment in which NIRS data is obtained from subjects performing a basic arithmetic task, and discuss the results.
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