Journal
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Volume -, Issue -, Pages 4549-4553Publisher
IEEE
DOI: 10.1109/embc.2019.8856436
Keywords
-
Funding
- University Hospital of Liege
- Belgian National Funds for Scientific Research (FRS-FNRS)
- European Union [785907]
- Luminous project [EU-H2020-fetopenga686764]
- European Space Agency (ESA)
- Belgian Federal Science Policy Of~ce (BELSPO)
- CenterTBI project [FP7-HEALTH-602150]
- Public Utility Foundation Universit Europenne du Travail
- Fondazione Europea di Ricerca Biomedica
- Bial Foundation
- Mind Science Foundation
- European Commission
- Marie Sklodowska-Curie [H2020-MSCA-IF-2016-ADOC-752686]
Ask authors/readers for more resources
Graph signal processing (GSP) is a novel approach to analyse multi-dimensional neuroimaging data, constraining functional measures by structural characteristics in a single framework (i.e. graph signals). In this approach, functional time series are assigned to the vertices of the underlying weighted graph and GSP analysis is performed in each time point of the signal. Here we used GSP to study local brain connectivity changes in patients with disorders of consciousness based on resting state high density electroencephalography (hdEEG) recordings. Total variation of the graph signals is a measure of signal smoothness over the underlying graph. In this study, we constructed the underlying graph based on the geometrical distances between each electrode pairs in such a way that local smoothness of the signal can be studied. Total variation analysis in alpha-band showed that in the pathological states of altered consciousness, local short range communication of brain regions in this frequency band is stronger than in healthy states which shows that information is segregated in local regions in patients with disorders of consciousness.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available