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Neural Eng."],"published-print":{"date-parts":[[2025,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    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.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We evaluated the\n                    <jats:italic>in silico<\/jats:italic>\n                    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 potential, alpha oscillations from human electroencephalogram (EEG), and hand movement kinematics from essential tremor patients) to identify the optimal model and parameters. We then validated the performance of an algorithm for estimating theta phase in real-time using rats implanted with electrodes in the hippocampus.\n                    <jats:italic>Results<\/jats:italic>\n                    . First, we found that signal amplitude and frequency variations strongly influence algorithm performance. Frequency-specific signal-to-noise ratio was positively correlated with performance (mean\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.42 across metrics), while amplitude and frequency variability were negatively correlated (mean\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.50 across metrics). Second, we showed that the length of the data window used for phase estimation is the key parameter for optimal performance of FFT-based algorithms, where the optimal data window length corresponds to the period of the oscillation (\u223c150 ms for hippocampal theta oscillations, \u223c100 ms for human EEG alpha, and \u223c125 ms for essential tremor kinematics). We validated this finding\n                    <jats:italic>in vivo<\/jats:italic>\n                    by estimating the phase of theta oscillations from the hippocampus of freely behaving rats, where a data window length corresponding to one theta cycle yielded the best performance across all metrics compared with shorter or longer window lengths.\n                    <jats:italic>Significance<\/jats:italic>\n                    . Our findings clarify the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms. We show that a data window length corresponding to one cycle of an oscillation can lead to improved performance.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ae10e1","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T22:49:42Z","timestamp":1759963782000},"page":"056037","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neurostimulation"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7062-4423","authenticated-orcid":true,"given":"Mengzhan","family":"Liufu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4773-4702","authenticated-orcid":true,"given":"Zachary M","family":"Leveroni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8636-5265","authenticated-orcid":false,"given":"Sameera","family":"Shridhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5891-366X","authenticated-orcid":true,"given":"Nan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0628-2531","authenticated-orcid":true,"given":"Jai Y","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"jneae10e1bib1","doi-asserted-by":"crossref","first-page":"4736","DOI":"10.1109\/EMBC.2018.8513232","type":"journal-article","article-title":"Continuous phase estimation for phase-locked neural stimulation using an autoregressive model for signal prediction","author":"Blackwood","year":"2018","journal-title":"2018 40th Annual Int. 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