Mengzhan Liufu, Zachary M Leveroni, Sameera Shridhar, Nan Zhou, Jai Y Yu
{"title":"基于相位特异性神经刺激的多种节律性生物信号实时相位检测优化。","authors":"Mengzhan Liufu, Zachary M Leveroni, Sameera Shridhar, Nan Zhou, Jai Y Yu","doi":"10.1088/1741-2552/ae10e1","DOIUrl":null,"url":null,"abstract":"<p><p>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, Hilbert Transform, and Phase Mapping) on three real-world biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha, and human essential tremor) to identify the optima model and parameters. We then validated the algorithm performance for estimating theta phase in real-time using rats implanted with electrodes in the hippocampus. 
Results
First, we found that signal amplitude and frequency variations strongly influence algorithm performance. Frequency-specific SNR was positively correlated with performance (mean R2 = 0.42 across metrics), while amplitude and frequency variability were negatively correlated (mean R2 = 0.50 across metrics). Second, we showed that the size of the data window used for phase estimation was the key parameter for optimal performance of FFT-based algorithms, where the optimal data window size corresponds to the period of the oscillation (~150 ms for hippocampal theta oscillations, ~100 ms for human EEG alpha, and ~125 ms for essential tremor). This data window length was validated in vivo for estimating the phase of theta oscillations from the hippocampus of freely behaving rats, where an input window size of one theta cycle yielded the best performance across all metrics compared with shorter or longer window sizes.
.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neurostimulation.\",\"authors\":\"Mengzhan Liufu, Zachary M Leveroni, Sameera Shridhar, Nan Zhou, Jai Y Yu\",\"doi\":\"10.1088/1741-2552/ae10e1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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, Hilbert Transform, and Phase Mapping) on three real-world biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha, and human essential tremor) to identify the optima model and parameters. We then validated the algorithm performance for estimating theta phase in real-time using rats implanted with electrodes in the hippocampus. 
Results
First, we found that signal amplitude and frequency variations strongly influence algorithm performance. Frequency-specific SNR was positively correlated with performance (mean R2 = 0.42 across metrics), while amplitude and frequency variability were negatively correlated (mean R2 = 0.50 across metrics). Second, we showed that the size of the data window used for phase estimation was the key parameter for optimal performance of FFT-based algorithms, where the optimal data window size corresponds to the period of the oscillation (~150 ms for hippocampal theta oscillations, ~100 ms for human EEG alpha, and ~125 ms for essential tremor). This data window length was validated in vivo for estimating the phase of theta oscillations from the hippocampus of freely behaving rats, where an input window size of one theta cycle yielded the best performance across all metrics compared with shorter or longer window sizes.
.</p>\",\"PeriodicalId\":94096,\"journal\":{\"name\":\"Journal of neural engineering\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of neural engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/1741-2552/ae10e1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neural engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1741-2552/ae10e1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neurostimulation.
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, Hilbert Transform, and Phase Mapping) on three real-world biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha, and human essential tremor) to identify the optima model and parameters. We then validated the algorithm performance for estimating theta phase in real-time using rats implanted with electrodes in the hippocampus.
Results
First, we found that signal amplitude and frequency variations strongly influence algorithm performance. Frequency-specific SNR was positively correlated with performance (mean R2 = 0.42 across metrics), while amplitude and frequency variability were negatively correlated (mean R2 = 0.50 across metrics). Second, we showed that the size of the data window used for phase estimation was the key parameter for optimal performance of FFT-based algorithms, where the optimal data window size corresponds to the period of the oscillation (~150 ms for hippocampal theta oscillations, ~100 ms for human EEG alpha, and ~125 ms for essential tremor). This data window length was validated in vivo for estimating the phase of theta oscillations from the hippocampus of freely behaving rats, where an input window size of one theta cycle yielded the best performance across all metrics compared with shorter or longer window sizes.
.