Subspace Learning with Interval-Censored Likelihoods for Dequantizing Percept PC LFP Snapshots

2026-09-18

AI Summary

研究针对神经刺激器感知信号量化问题，采用区间删失子空间估计方法特别是量化概率PCA，减少虚假峰值并保持真实峰值检测。

Abstract

Implanted neurostimulators that sense local field potentials now enable chronic electrophysiology based biomarker tracking in patients at home. The Medtronic Percept PC, the only commercially available sensing-enabled deep brain stimulation (DBS) device, stores spectral amplitudes as 16-bit integers at approximately 0.1 $μ$V per bit (quantum $q \approx 0.11$ $μ$Vp). At frequencies where the true amplitude spans only a few quantization levels, consecutive bins round to the same stored value. Standard spectral parameterization (FOOOF, fitting oscillations and one over f), which separates periodic peaks from the aperiodic 1/f activity, treats every value as exact and fits oscillatory peaks to these plateaus. Because these spectra feed clinical biomarker pipelines and spectral foundation models for symptom decoding, spurious peaks can corrupt downstream inference. Across 9,438 spectra from 14 hemispheres in 7 subcallosal cingulate DBS patients, 20.6% of peaks detected at [2, 45] Hz have no match in ground truth synthesized by quantizing clean in-clinic BrainSense recordings, while aggregate beta band power and the aperiodic exponent are preserved. We formalize dequantization as interval-censored subspace estimation and compare five classes of correction methods. Quantized probabilistic PCA is the only tested method that reduces the spurious rate (20.6% to 18.3%) while preserving true peak detection and keeping noise floor RMSE below $q/\sqrt{12}$.

Problem

• Dequantization
• Interval-Censored Likelihoods
• Subspace Learning
• Local Field Potentials
• Spectral Analysis

Innovation

• interval-censored likelihoods
• subspace learning
• dequantization
• quantized probabilistic PCA
• spurious peak reduction