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Difference between revisions of "Compression of iEEG Data"

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[[Category:Digital]][[Category:Semester Thesis]][[Category:Available]] [[Category:2020]][[Category:Hot]][[Category:Human Intranet]][[Category:xiaywang]][[Category:Herschmi]][[Category:Epilepsy]]
[[Category:Digital]][[Category:Semester Thesis]][[Category:Available]] [[Category:2020]][[Category:Hot]][[Category:Human Intranet]][[Category:xiaywang]][[Category:Epilepsy]]

Latest revision as of 08:28, 16 September 2021


Seizure detection systems hold promise for improving the quality of life for patients with epilepsy that afflicts nearly 1% of the world’s population. High-resolution intracranial Electroencephalography (iEEG) enables the detection and location of such seizures. When aiming a low power implanted system the large amount of data has to be efficiently reduced. iEEG signals are sparse and have been successfully compressed using well-established encoders such as Discrete Wavelet Transform (DWT) or Non-Negative Matrix Factorization (NNMF) [1]. Due to its recent success, however, convolutional neural networks (CNNs) are getting more attention and have shown to be a viable option to compress EEG signals [2]. This project compares deep convolutional autoencoders with state-of-the-art DWT and NNMF to compress iEEG data from long-term recordings of epileptic patients. We use the publicly available long-term dataset [3] consisting of a total of 2656 hours iEEG recordings.

Status: Available

Looking for student for Master's thesis or Semester project.

Supervision: Xiaying Wang


  • Machine Learning
  • Python & C Programming


20% Theory
80% Programming


Luca Benini

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  • [1] M. Baud, et al., Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy, 2018
  • [2] A. Al-Marridi, et al., Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems, 2018
  • [3] iEEG-SWEZ data base

Practical Details

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