Speaker
Dora Hermes, PhD
Date
Location
University of Houston
Abstract
Many different signals can be measured from the human brain, each driven by different
underlying physiology. The interpretation of these mesoscale signals is essential to develop
biomarkers of neurological diseases, but presents a complex problem as these signals reflect
the aggregate physiology from neuronal populations. I will show a series of studies of the
intracranial EEG (iEEG) signals in human visual cortex and show how these can be predicted
from the visual inputs using computational models. I will discuss the effects of underlying
dynamics such as neuronal synchrony on these measurements and show how some signals
can be used in settings of brain-machine interfaces or to identify biomarkers of neurological
disease.
underlying physiology. The interpretation of these mesoscale signals is essential to develop
biomarkers of neurological diseases, but presents a complex problem as these signals reflect
the aggregate physiology from neuronal populations. I will show a series of studies of the
intracranial EEG (iEEG) signals in human visual cortex and show how these can be predicted
from the visual inputs using computational models. I will discuss the effects of underlying
dynamics such as neuronal synchrony on these measurements and show how some signals
can be used in settings of brain-machine interfaces or to identify biomarkers of neurological
disease.