  On image data, data augmentation is becoming less relevant due to the large
amount of available training data and regularization techniques. Common
approaches are moving windows (cropping), scaling, affine distortions, random
noise, and elastic deformations. For electroencephalographic data, the lack of
sufficient training data is still a major issue. We suggest and evaluate
different approaches to generate augmented data using temporal and
spatial/rotational distortions. Our results on the perception of rare stimuli
(P300 data) and movement prediction (MRCP data) show that these approaches are
feasible and can significantly increase the performance of signal processing
chains for brain-computer interfaces by 1% to 6%.
