Mitigating Mismatch Compression in Differential Local Field Potentials
Vineet Tiruvadi, Sam James, Bryan Howell, Mosadoluwa Obatusin, Andrea Crowell, Patricio Riva-Posse, Ki Sueng Choi, Allison Waters, Robert E. Gross, Cameron C. McIntyre, Helen S. Mayberg, Robert Butera
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Abstract
Bidirectional deep brain stimulation (bdDBS) devices capable of recording differential local field potentials (dLFP) enable neural recordings alongside clinical therapy. Efforts to identify objective signals of various brain disorders, or disease readouts, are challenging in dLFP, especially during active DBS. In this report we identified, characterized, and mitigated a major source of distortion in dLFP that we introduce as mismatch compression (MC). MC occurs secondary to impedance mismatches across the dLFP channel resulting in incomplete rejection of artifacts and downstream amplifier gain compression. Using in silico and in vitro models we demonstrate that MC accounts for impedance-related distortions sensitive to DBS amplitude. We then use these models to develop and validate a mitigation strategy for MC that is provided as an opensource library for more reliable oscillatory disease readouts.