Rotational Data Augmentation for Electroencephalographic Data
Mario Michael Krell, Su-Kyoung Kim
In Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (EMBC-17), 11.7.-15.7.2017, JeJu Island, South Korea, IEEE, Jul/2017.
Abstract
:
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.
Files:
20170502_Rotational_Data_Augmentation_for_Electroencephalographic_Data.pdf
Links:
https://ieeexplore.ieee.org/document/8036864