Real-time Sub-cycle Oscillatory Beta Burst Detection

Beta-band burst activity is a key biomarker of Parkinsonian pathophysiology and a control signal for adaptive deep brain stimulation (aDBS). Existing burst detection approaches rely on bandpass filtering followed by envelope extraction and thresholding, which introduces onset/offset latency due to sliding-window estimation and can be unstable under variations in signal-to-noise ratio or threshold choice. We introduce a switching state space approach for real-time burst detection that models oscillatory activity as a superposition of latent harmonic components and explicitly represents burst and non-burst regimes. The framework performs real-time inference using a set of Kalman filters and computes posterior mode probabilities using a Markov transition prior, producing sample-by-sample burst probabilities together with phase and uncertainty estimates. In simulated data with sinusoidal beta bursts embedded in both white and pink noise, the method improved burst detection accuracy, reduced onset latency and offset latency compared with causal envelope-based baselines, and showed reduced sensitivity to threshold selection. In analysis of real sub-thalamic nucleus recordings during a grip-force task, the MSSR method recovered movement-related burst modulation consistent with prior reports. Our results indicate that state space switching provides a principled route to low-latency burst detection that may better support closed-loop stimulation strategies timed to beta bursts.