Data augmentation for self-paced motor imagery classification with C-LSTM

Brain-computer interfaces (BCI) are becoming important tools for assistive technology, particularly through the use of motor imagery (MI) for aiding task completion. However, most existing methods of MI classification have been applied in a trial-wise fashion, with window sizes of approximately 2 s or more. Application of this type of classifier could cause a delay when switching between MI events.

In this study, state-of-the-art classification methods for motor imagery are assessed offline with considerations for real-time and self-paced control, and a convolutional long-short term memory (C-LSTM) network based on filter bank common spatial patterns (FBCSP) is proposed. In addition, the effects of several methods of data augmentation on different classifiers are explored.

The results of this study show that the proposed network achieves adequate results in distinguishing between different control classes, but both considered deep learning models are still less reliable than a Riemannian MDM classifier. In addition, controlled skewing of the data and the explored data augmentation methods improved the average overall accuracy of the classifiers by 14.0% and 5.3%, respectively.

This manuscript is among the first to attempt combining convolutional and recurrent neural network layers for the purpose of MI classification, and is also one of the first to provide an in-depth comparison of various data augmentation methods for MI classification. In addition, all of these methods are applied on smaller windows of data and with consideration to ambient data, which provides a more realistic test bed for real-time and self-paced control.