AI 中文总结
针对Percept PC LFP频谱量化导致的虚假峰问题，提出区间截尾子空间估计框架，量化概率PCA有效降低虚假峰率并保持真实峰检测。
AI 中文摘要
植入式神经刺激器能够感知局部场电位，现在使得患者在家中进行基于慢性电生理学的生物标志物追踪成为可能。美敦力Percept PC是唯一市售的具备感知功能的脑深部电刺激（DBS）设备，它以16位整数存储频谱幅度，每个比特约0.1微伏（量子q≈0.11微伏峰值）。在真实幅度仅跨越几个量化水平的频率处，连续的频段会四舍五入到相同的存储值。标准频谱参数化方法（FOOOF，拟合振荡和1/f）将周期峰与非周期1/f活动分开，将每个值视为精确值，并将振荡峰拟合到这些平台区。由于这些频谱为临床生物标志物流程和用于症状解码的频谱基础模型提供数据，虚假峰可能破坏下游推断。在来自7名胼胝体下扣带回DBS患者的14个半球的9,438个频谱中，在[2,45]赫兹范围内检测到的峰中有20.6%在通过量化干净临床BrainSense记录合成的地面真值中没有匹配，而聚合β频带功率和非周期指数得以保留。我们将去量化形式化为区间截尾子空间估计，并比较五类校正方法。量化概率主成分分析是唯一测试的能够降低虚假率（从20.6%降至18.3%）同时保持真实峰检测并将噪声底限均方根误差保持在q/√12以下的方法。
英文摘要
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 $q/\sqrt{12}$.
CommentsAccepted at the 2026 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Atlanta, USA. 4 figures, 2 tables
