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

Journal of Neural Engineering
E Kinney-LangJ Escudero

Abstract

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...Continue Reading

References

Feb 1, 1988·Electroencephalography and Clinical Neurophysiology·T GasserL Sroka
Apr 21, 1999·Brain Research. Brain Research Reviews·W Klimesch
Jun 28, 2000·Circulation·J J GoldbergerS Inbar
Jul 26, 2002·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Peter J MarshallNathan A Fox
Jan 22, 2011·Computational Intelligence and Neuroscience·Robert OostenveldJan-Mathijs Schoffelen
Mar 23, 2012·Sensors·Luis Fernando Nicolas-Alonso, Jaime Gomez-Gil
Dec 6, 2014·Frontiers in Neuroengineering·Aleksandra VuckovicChristoph Guger
Dec 10, 2014·Neurobiology of Disease·Surjo R SoekadarLeonardo G Cohen
Jan 24, 2015·Annals of Physical and Rehabilitation Medicine·L E H van DokkumI Laffont
Apr 4, 2015·Journal of Neuroscience Methods·Fengyu CongTapani Ristaniemi
Jan 1, 2016·Journal of Neurophysiology·Natalie Mrachacz-KerstingDario Farina
Oct 25, 2017·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·E Kinney-LangJ Escudero

❮ Previous
Next ❯

Related Concepts

Related Feeds

Brain-Computer Interface

A brain-computer interface, also known as a brain-machine interface, is a bi-directional communication pathway between an external device and a wired brain. Here is the latest research on this topic.