Difference between revisions of "Data Augmentation Techniques in Biosignal Classification"
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| − | [[Category:Digital]][[Category:Semester Thesis]][[Category:Master Thesis]] [[Category:Available]] [[Category:2020]][[Category:Hot]][[Category:Human Intranet]][[Category:xiaywang]] | + | [[Category:Digital]][[Category:Semester Thesis]][[Category:Master Thesis]] [[Category:Available]] [[Category:2020]][[Category:Hot]][[Category:Human Intranet]][[Category:xiaywang]][Category:Epilepsy]][[Category:BCI]] |
==Description== | ==Description== | ||
Revision as of 09:24, 16 September 2021
[Category:Epilepsy]]
Contents
Description
In many biosignal classification tasks, simple and robust classifiers are preferred compared to more sophisticated and potentially more accurate ones. This is mostly due to the lack of labeled training data. Especially for biomedical applications, data acquisition and labeling are very expensive and time-consuming. In this thesis, the student explores data augmentation methods [1][2] to improve the learning in biosignal classification tasks. Applications range from ECG to EEG [3] classification tasks.
Status: Available
Looking for student for Master's thesis or Semester project.
- Supervision: Xiaying Wang
Prerequisites
- Machine Learning
- Python & C Programming
Character
- 20% Theory
- 80% Programming
Professor
Literature
- [1] Q. Wen, et al., Time Series Data Augmentation for Deep Learning: A Survey, 2020
- [2] M. M. Krell, et al., Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data, 2018
- [3] T. M. Ingolfsson, et al., EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces, 2020
Practical Details
