Continuous phase estimation for phase-locked neural stimulation using an autoregressive model for signal prediction.
Open accessAbstract
Neural oscillations enable communication between brain regions. Closed-loop brain stimulation attempts to modify this activity by stimulation locked to the phase of concurrent neural oscillations. If successful, this may be a major step forward for clinical brain stimulation therapies. The challenge for effective phase-locked systems is accurately calculating the phase of a source oscillation in real time. The basic operations of filtering the source signal to a frequency band of interest and extracting its phase cannot be performed in real time without distortion. We present a method for continuously estimating phase that reduces this distortion by using an autoregressive model to predict the future of a filtered signal before passing it though the Hilbert transform. This method outperforms published approaches on real data and is available as a reusable open-source module. We also examine the challenge of compensating for the filter phase response and outline promising directions of future study.
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Different algorithms have been proposed for continuous phase estimation in real-time.
“although different algorithms have been proposed for continuous phase estimation in real-time (Kim et al., 2016; Blackwood et al., 2018)”
From Toward a personalized closed-loop stimulation of the visual cortex: Advances and challenges (2022) · cites this paper as Blackwood et al., 2018
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- Authors
- Blackwood, E., Lo, M.-C., & Widge, A. S.
- Affiliations
- Harvard University (US)
- Venue
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2018-07
- Volume
- 2018
- Pages
- 4736-4739
- ISSN
- 2375-7477 (ISSN-L)
- PMC
- PMC6768559
- OpenAlex
- W2898645603
- Cited by
- 22
- References
- 7
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 4 · 2020: 4 · 2019: 4 · 2018: 1
- FWCI
- 5.58
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R21MH113103, R21MH109722)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Keywords
- phase estimation, closed-loop brain stimulation, autoregressive models, signal prediction, Hilbert transform, neural oscillations, open source software
- MeSH terms
- Models, Neurological, Neurons
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