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

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

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School of Engineering, Institute for Digital Communications, The University of Edinburgh, Alexander Graham Bell Building, Edinburgh EH9 3FG, United Kingdom. The Muir Maxwell Epilepsy Centre, The University of Edinburgh, Edinburgh EH8 9XD, United Kingdom.



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.


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.


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.


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


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