ArticleJournal of neuroscience methods2021
A user-friendly algorithm for adaptive closed-loop phase-locked stimulation.
Article in Journal of neuroscience methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neurostimulation.Journal of neural engineering · 2025Article
- The Effect of the Peristimulus α Phase on Visual Perception through Real-Time Phase-Locked Stimulus Presentation.eNeuro · 2023Article
- Toolkit for Oscillatory Real-time Tracking and Estimation (TORTE).Journal of neuroscience methods · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundClosed-loop phase-locked stimulation experiments are rare due to the unavailability of user-friendly algorithms and devices. Our goal is to provide an algorithm for the detection of oscillatory activity in local field potentials (LFPs) and phase prediction, which is user-friendly and robust to non-stationarities in LFPs of behaving animals. NEW
methodWe propose an algorithm that only requires specification of the frequency range within which oscillatory episodes are tracked. Frequency-specific detection thresholds and filter parameters are adjusted automatically based on the short-time LFP power spectrum. Estimates of instantaneous frequency and instantaneous phase are used for phase extrapolation, taking advantage of Bayesian estimation. We used real LFP signals, recorded from a variety of different species and different brain areas, as well as artificial LFP signals with known properties to assess the detection and prediction performance of our algorithm and three previously published reference algorithms under various conditions. RESULTS AND COMPARISON WITH EXISTING
methodsOur algorithm, while significantly more user-friendly than previous approaches, provides a solid detection and prediction performance over a wide range of realistic conditions and, in many cases, has a longer prediction horizon than the reference algorithms. Due to its ability to adjust to changes in the signal, the algorithm is well-prepared to deal with non-stationarities in oscillation frequency, even in the presence of multiple oscillation components.
conclusionsWe have created a universal algorithm for oscillation detection and phase prediction, which performs well and is user-friendly at the same time, making closed-loop phase-locked stimulation experiments easier to accomplish.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.