University of Illinois Urbana-Champaign

Extracting interpretable features from large scale clinical EEGs using unsupervised learning

Gupta, Teja Borra

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https://hdl.handle.net/2142/120453

Description

Title
Extracting interpretable features from large scale clinical EEGs using unsupervised learning
Author(s)
Gupta, Teja Borra
Issue Date
2023-05-03
Director of Research (if dissertation) or Advisor (if thesis)
Varatharajah, Yogatheesan
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • EEG
  • neurological disorders, tensor decomposition
  • unsupervised learning
  • interpretability
  • variational autoencoder
Abstract
Analyzing clinical electroencephalograms (EEG) is crucial for diagnosing and monitoring neurological disorders. However, manual expert review is not scalable and is prone to errors. Thus, more efficient and reliable methods are needed. Current methods rely on two-dimensional decompositions such as principal component analysis (PCA) or indepedent component anal ysis (ICA) and deep learning methods such as autoencoders (AE) and self-supervised learning (SSL). However, these methods do not retain the naturalstructure of the data and not easily interpretable. To overcome these limitations, we propose using tensor decomposition (TD) to extract interpretable and clinically useful features from EEGs. Tensor decomposition retains the natural structure of the data and provides a more efficient and reliable alternative to traditional approaches. Additionally, to address the lack of expressivity with tensor decomposition, we explore ways to incorporate tensor decomposition with the variational autoencoder framework.
Graduation Semester
2023-05
Type of Resource
Thesis
Copyright and License Information
Copyright 2023 Teja Gupta

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