Biological and biomedical signals, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. This work proposes advanced statistical signal processing and machine learning methodologies to assess neurostimulation from neural recordings. This is done using adaptive processing and unsupervised clustering methods applied to neural recordings to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.