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Data Augmentation Techniques in Biosignal Classification

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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: Michael Hersche, Xiaying Wang

Prerequisites

  • Machine Learning
  • Python & C Programming


Character

20% Theory
80% Programming

Professor

Luca Benini

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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

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