|
Michael Breakspear
The Brain Dynamics Centre, University of New South Wales
Bimodal and extremum statistics in human EEG : Measurement and implications
Wednesday 9th April 14:05-14:55pm,
Eastern Avenue Lecture Theatre.
Although nonlinear dynamics are known to determine the behaviour of
individual neurons, an emerging consensus is that large-scale
neocortical activity can be characterised as a Gaussian process. This
view arises from both modelling and behavioural studies of the brain.
Hence nonlinearity at this scale is seen to herald pathological states
such as seizures. We analysed the temporal fluctuations in human
electrocencephalographic (EEG) recordings acquired from healthy human
subjects and estimated the likely probability distribution function(s)
across a range of temporal scales. At many time scales (e.g. 20 Hz)
such fluctuations deviate significantly from fitted Gaussian
distributions, with a bias towards a power-law scaling at the high
amplitude end, reflecting extremal events in the EEG which would not
be expected to occur in a Gaussian field. Fits to the data can be
better captured by the exponential family of noise distributions.
Within the traditional alpha range (~10 Hz), activity typically shows
a distinct bimodal distribution. Hence, whilst Gaussian models capture
much of the signal variation in macroscopic brain signals, they are
unable to explain these distinct phenomena. Are such deviations from a
Gaussian model important and what alternative models, such as
fractional kinetics, should be explored?
|
|
|
|
|
|
|
|