Tensor-driven extraction of developmental features from varying paediatric EEG datasets.
Journal:
Journal of neural engineering
Published Date:
May 21, 2018
Abstract
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.