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Deep Convolutional Autoencoder for iEEG Signals

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Non-EEG Seizure.jpg


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 Compressive Sensing (CS), Discrete Wavelet Transform (DWT), or Non-Negative Matrix Factorization (NNMF). Due to its recent success, however, convolutional neural nets (CNNs) are getting more attention and showed to be a viable option to compress EEG signals [1,2].

In this thesis, the students will develop a deep convolutional autoencoder to compress iEEG signals.

Status: Completed

Simon Hungerbühler
Supervision: Michael Hersche, Xiaying Wang, Lukas Cavigelli, Abbas Rahimi


  • Machine Learning
  • Linear Algebra
  • Python Programming


20% Theory
80% Programming


Luca Benini

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  • Tingxi Wen et. al., Deep Convolution Neural Network and Autoencoders-Base Unsupervised Feature Learning of EEG Signals [1]
  • Abeer Z. Al-Marridi et. al., Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems [2]

Practical Details

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