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Elucidating age-specific patterns from background electroencephalogram pediatric datasets via PARAFAC

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3797-3800
Number of pages4
ISBN (Electronic)978-1-5090-2809-2
ISBN (Print)978-1-5090-2810-8
DOIs
Publication statusPublished - 14 Sep 2017
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Jeju Island, Korea, Republic of
Duration: 11 Jul 201715 Jul 2017
https://embc.embs.org/2017/

Publication series

Name
ISSN (Print)1557-170X
ISSN (Electronic)1558-4615

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC 2017
CountryKorea, Republic of
CityJeju Island
Period11/07/1715/07/17
Internet address

Abstract

Brain-computer interfaces (BCI) have the potential to provide non-muscular rehabilitation options for children. However, progressive changes in electrophysiology throughout development may pose a potential barrier in the translation of BCI rehabilitation schemes to children. Tensors and multiway analysis could provide tools which help characterize subtle developmental changes in electroencephalogram (EEG) profiles of children, thus supporting translation of BCI paradigms. Spatial, spectral and subject information of age-matched pediatric subjects in two EEG datasets were used to form 3-dimensional tensors for use in parallel factor analysis (PARAFAC) and direct projection comparison. Within dataset cross-validation results indicate PARAFAC can extract age-sensitive factors which accurately predict subject age in 90% of cases. Cross-dataset validation revealed extracted age-dependent factors correctly identified age in 3 of 4 test subjects. These findings demonstrate that tensor analysis can be applied to characterize the age-specific subtleties in EEG, which provide a means for tracking developmental changes in pediatric rehabilitation BCIs.

Event

39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

11/07/1715/07/17

Jeju Island, Korea, Republic of

Event: Conference

ID: 34857478