Optimizing electrode placement and information capacity for local field potentials in cortex
The paper develops subject-specific, in silico modeling tools to optimize intracranial electrode placement and maximize the Shannon-Hartley information capacity of local field potentials (LFPs). Using subject MRI data and finite element modeling to generate sensitivity/lead-field maps, it compares electrode placements, contact sizes, contact configurations, and substrate properties across subdural and intracortical devices, including using a genetic algorithm for placement optimization and SEPIO for selecting sensor subsets for source classification. It reports that optimized placements improve information capacity and signal quality and that sensor-based classification can enhance data quality without increasing electrode cost. The paper’s main limitation is that the results are demonstrated through computational/simulation-based modeling rather than direct clinical outcome validation. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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- last seen: 2026-05-20T01:45:00.602351+00:00