Bagging Algorithm Based on Possibilistic Data Interpolation for Brain-Computer Interface

Abstract

Recently, brain-computer interfaces (BCIs) and brain-machine interfaces have garnered the attention of researchers. Based on connections with external devices, external computers and machines can be controlled by brain signals measured via near-infrared spectroscopy (NIRS) or electroencephalograph devices. Herein, we propose a novel bagging algorithm that generates interpolation data around misclassified data using a possibilistic function, to be applied to BCIs. In contrast to AdaBoost, which is a conventional ensemble learning method that increases the weight of misclassified data to incorporate them with high probability to the next datasets, we generate interpolation data using a membership function centered on misclassified data and incorporate them into the next datasets simultaneously. The interpolated data are known as virtual data herein. By adding the virtual data to the training data, the volume of the training data becomes sufficient for adjusting the discriminate boundary more accurately. Because the membership function is defined as a possibility distribution, this method is named the bagging algorithm based on the possibility distribution. Herein, we formulate a bagging-type ensemble learning based on the possibility distribution and discuss the usefulness of the proposed method for solving simple calculations using NIRS data.