Brainwave Classification Using Covariance-Based Data Augmentation

Abstract

A brain-machine interface (BMI) is a technology that controls machines via brainwaves. In BMI, the performance of brainwave analysis is very important for achieving machine control that reflects the user's intention. One of the main obstacles in this analysis is an insufficient amount of data points because long-term brain signal experiments tend to reduce data quality. Data augmentation methods can be used to overcome this limitation. Recently, several neural network-based data augmentation methods have been developed. However, those methods have several limitations; first, they require considerable computation time because a very large number of parameters must be obtained. Moreover, the neural network based method can suffer from unstable training, which results in quality degradation of artificial data. To address these problems, this paper introduces a method that generates an artificial dataset which has correlation of feature similar to the original dataset. Specifically, after decomposing the covariance matrix for the features into a lower triangular matrix, an artificial dataset can be generated by multiplying the lower triangular matrix by random variables. This method is computationally fast, and the augmentation is stable. When the brainwave data were augmented using this method, classification performance was improved by 1.08%-6.72%. This method focuses on mean, correlation, and not taking into account the other statistical parameters. Since it rapidly generates a large dataset, it can also be useful in other applications.