Component-mixing strategy: A decomposition-based data augmentation algorithm for motor imagery signals

Deep learning has achieved a remarkable success in areas such as brain-computer interface systems (BCI). However, electroencephalography (EEG) signals evoked by motor imagery (MI) are sometimes limited in their amount due to invalid data caused by the subjects' fatigue, leading to a performance degradation. To this end, in this work we extend empirical mode decomposition into multivariate empirical mode decomposition and intrinsic time-scale decomposition, proposing a component-mixing strategy (CMS) for MI data augmentation. Compared to commonly used data augmentation methods such as generative adversarial networks, CMS can generate artificial trials from a few training samples without any required training. We claim that raw and artificial data generated by CMS are consistent with respect to the distribution and power spectral density. Experiments done on the BCI Competition IV dataset 2b show that CMS can achieve a considerable improvement on the binary classification accuracy and the area under the curve score using EEGNet, wavelet neural networks and a support vector machine. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a component-mixing strategy (CMS) for motor imagery (MI) data augmentation, which extends empirical mode decomposition into multivariate empirical mode decomposition and intrinsic time-scale decomposition. CMS can generate artificial trials from a few training samples without required training and has been shown to significantly improve binary classification accuracy and area under the curve scores using different algorithms on the BCI Competition IV dataset 2b.