Signal Analysis techniques are routinely used in Neuroscience to interpret raw signals harvested from the Nervous System. From a simple Fourier analysis to more complicated methods such as multiresolution wavelet analysis, such techniques must be used for signal manipulation in order to reach informed conclusions on the measurements taking place.

In neuroscientific research, it is common practice to scan a neural recording manually to label the areas of the signal that are relevant to the research at hand. This can, obviously, be very time-consuming for the researchers. What is more, this method can prove imperfect, seeing as two different researchers can disagree on the labeling of the data.

In every experiment, signal epochs are isolated because they stand out from the rest of the recording due to a special characteristic, which is, in the previous case, visible with the unaided eye. In signal processing terms, this means that the signal displays specific spectrotemporal characteristics during these epochs. Thus, these characteristics can be isolated, quantified and studied independently, while a detection algorithm can be developed so that the detection and labeling of the significant signal epochs can be carried out automatically.

In this project, the spontaneous activity of the neurons in the Prefrontal Cortex was analyzed in relation to neural synchrony, using time-varying ARModels. It was concluded that the signal epochs of neural synchrony display common characteristics besides being visually similar. This allowed the isolation of the synchrony epochs based on model parameters.

However, the training of the models is very computationally intensive, so a detection algorithm was developed, based on a matched filter which made use of one of the isolated epochs as a template. The detection scheme was then validated using a recording harvested during electrical stimulation of the deep brain, evaluating the quality of the scheme was evaluated.

Finally, the detection scheme was applied to stimulation recordings to study the electrical behavior of the Prefrontal Cortex during electrical stimulation of deep brain structures.