Tensor-driven extraction of developmental features from varying paediatric EEG datasets

J Neural Eng. 2018 Aug;15(4):046024. doi: 10.1088/1741-2552/aac664. Epub 2018 May 21.


Objective: Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG.

Approach: Three paediatric datasets ([Formula: see text]) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed stochastic neighbour embedding (t-SNE) maps.

Main results: Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features.

Significance: The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Child
  • Child, Preschool
  • Cohort Studies
  • Databases, Factual*
  • Electroencephalography / methods*
  • Epilepsy / diagnosis
  • Epilepsy / physiopathology*
  • Female
  • Humans
  • Machine Learning*
  • Male
  • Retrospective Studies
  • Signal Processing, Computer-Assisted*
  • Young Adult