Rotational data augmentation for electroencephalographic data

Motivation: For deep learning on image data, a common approach is to augment the training data by artificial new images, using techniques like moving windows, scaling, affine distortions, and elastic deformations. In contrast to image data, electroencephalographic (EEG) data suffers even more from the lack of sufficient training data. Methods: We suggest and evaluate rotational distortions similar to affine/rotational distortions of images to generate augmented data. Results: Our approach increases the performance of signal processing chains for EEG-based brain-computer interfaces when rotating only around y- and z-axis with an angle around ±18 degrees to generate new data. Conclusion: This shows that our processing efficient approach generates meaningful data and encourages to look for further new methods for EEG data augmentation.