Abstract
We present a probabilistic model for classification of micro-EEG signals, recorded during deep brain stimulation surgery for Parkinson's disease. The model uses parametric representation of neuronal background activity, estimated using normalized root-mean-square of the signal.
Contrary to existing solutions using Bayes classifiers or Hidden Markov Models, our model uses smooth state-transitions represented by sigmoid functions, which ensures flexible model structure in combination with general optimizers for parameter estimation and model fitting. The presented model can easily be extended with additional parameters and constraints and is intended for fitting of a 3D anatomical model to micro-EEG data in further perspective.
In an evaluation on 260 trajectories from 61 patients, the model showed classification accuracy 90.0%, which was comparable to existing solutions. The evaluation proved the model successful in target identification and we conclude that its use for more complex tasks in the area of DBS planning and modeling is feasible.