Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neurostimulation
Mengzhan Liufu, Zachary M Leveroni, Sameera Shridhar, Nan Zhou, Jai Y Yu
TL;DR
This study improves real-time phase detection in brain signals to better time neurostimulation, finding that matching the data window to the oscillation cycle boosts accuracy.
Contribution
The study identifies optimal data window lengths for FFT-based phase detection algorithms tailored to specific oscillation frequencies in biological signals.
Findings
Signal amplitude and frequency variations strongly influence phase detection algorithm performance.
Optimal data window length corresponds to the oscillation period (e.g., 150 ms for hippocampal theta oscillations).
In vivo validation in rats confirmed that one-cycle window length yields best phase estimation performance.
Abstract
Objective. Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on the fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. Approach. We evaluated the in silico performance of three phase detection algorithms [Endpoint-corrected Hilbert transform (ecHT), Hilbert transform (HT), and phase mapping (PM)] on three real-world biological signals with distinct spectral properties (theta oscillations from rodent hippocampal local field…
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Taxonomy
TopicsNeurological disorders and treatments · Neuroscience and Neural Engineering · EEG and Brain-Computer Interfaces