  To define the neural networks responsible of the epileptic seizure, we had to
study the electrophysiological signal in a proper way. The early recognition of
the seizure build up could also be defined through the time space mapping of
the preictal gamma oscillations. The electrophysiological signals present three
types of wave: oscillations, spikes, and a mixture of both. Recent studies
prove that spikes and oscillations should be separated efficiently to define
the accurate neural connectivity for each activity. However retrieving the
transitory activity is a sensitive task due to the frequency interfering
between the gamma oscillatory and the transitory activities. Many filtering
techniques are highlighted to ensure a good separation: reducing false
oscillations in the transitory activity and vis versa. However and for a big
data set, this separation necessitate a big consumption in time execution, this
constraint will be overcome using embedded architecture. The integration of
these filtering techniques would also lead to creating instantaneous monitoring
of the seizure recognition, build up and neurofeedback devices. We propose here
to implement the stationary wavelet transform as a convenient filtering
technique to keep only the preictal gamma oscillations on a partial dynamic
configuration, then we will use the same architecture to integrate the time
space mapping for an early recognition of the build up seizure. We proved a
faster recognition of the build up seizure through the non contaminated
preictal gamma oscillations time space mapping (about 40 times faster),
obtained by the integration of the wavelet transform.
