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Deep Brain Stimulation (DBS) has continuously gained popularity as a symptomatic treatment in diseases such as Parkinson’s Disease (PD), Essential Tremor (ET), and dystonia. For better understanding the mechanisms of DBS, a series of intraoperative Local Field Potential (LFP) recordings are acquired from patients during DBS. These recordings are vastly affected by stimulation artifacts (SAs). Despite the recent advancements in digitaland analog-based processing methods in removing SAs, a common approach in Neuroscience community is to delete an entire time interval affected by such artifact (lasting about 5 milliseconds after the onset of DBS pulse). In this paper, we propose a robust computational framework based on adaptive filtering strategy to automatically estimate the artifact induced by each individual DBS pulse, and to recover the neural response during the artifact. An estimate of the common identical artifact is obtained by fitting a B-Spline smoothing function to the average of all recordings followed by DBS pulse. The common artifact, for each individual pulse, is then fed to a Normalized Least Mean Square (NLMS) adaptive filter whose error is equal to the difference between the recorded data and the adapted artifact i.e., the recovered neural response. This framework is then confirmed using the LFP recorded from patients with PD at the level of Subthalamic Nucleus (STN). The artifact is visibly and quantifiably diminished after ~ 1.5 msec after the onset of DBS pulse. This will allow researchers to peek further into the mechanism of action and health effects of DBS. We believe that this work will broaden window of clarity will pave the way for the development of accurate bidirectional closed-loop DBS techniques.

Adaptive Filters to Remove Deep Brain Stimulation Artifacts from Local Field Potentials