Valente, Sabrina, Ringwood, John, Mangourova, Violeta and Lowry, John P. (2012) Investigation of events in the EEG signal correlated with changes in both oxygen and glucose in the brain. In: ISSC 2012, 28-29 June, 2012, NUI Maynooth.
PDF
JR_EEG_Signal.pdf
Download (224kB)
JR_EEG_Signal.pdf
Download (224kB)
Abstract
Since the brain has no constant energy reserves, a continuous supply of energy
substrates is central to all processes that maintain the functionality of the neuronal cells.
EEG has been found to be tightly related to variations in the concentration of the energy
substrates such as oxygen and glucose. Prediction of neural activation is particularly useful
as it could contribute significantly in the prevention, stabilization, or treatment of diseases
such as Alzheimer's disease, migraine headache, and ischemic stroke, in which signaling
between neurons and brain vessels is threatened because of dysfunctions that affect the
neuronal, astroglial, and/or vascular components of the neurovascular unit. This work deals
with investigation of events in the EEG signal correlated with changes in both oxygen and
glucose signals in the brain. The topic is to implement a model that through measures of
oxygen and glucose in the brain of rats allow to achieve a good estimation of the neural
signals, which reflecting the simultaneous metabolic changes, during spontaneous oscillation
and electrical stimulation.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Keywords: | neuronal activation; brain metabolism; energy substrates; system identification; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering |
Item ID: | 3864 |
Depositing User: | Professor John Ringwood |
Date Deposited: | 17 Sep 2012 11:04 |
Refereed: | Yes |
URI: | https://mu.eprints-hosting.org/id/eprint/3864 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year