ArticleJournal of neural engineering2020
Experimental evaluation of methods for real-time EEG phase-specific transcranial magnetic stimulation.
Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Transcranial magnetic stimulation treatment in Alzheimer's disease: a meta-analysis of its efficacy as a function of protocol characteristics and degree of personalization.Journal of neurology · 2022Pooled it
- Manipulating speech perception with amplitude-modulated kilohertz magnetic perturbation (AM-kTMP).bioRxiv : the preprint server for biology · 2026Article
- Integrating multidimensional nociceptive-related cortical features for unsupervised assessment of anesthesia states in rats.iScience · 2026Article
- Advancing Dry Electroencephalography With Scalable, Soft, and Transcranial Magnetic Stimulation-Compatible TiAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bayesian Temporal Prediction: A Robust Algorithm for Real-Time EEG Phase-Dependent Brain Stimulation.IEEE transactions on bio-medical engineering · 2026Article
- Lightweight EEG Phase Prediction Based on Channel Attention and Spatio-Temporal Parallel Processing.Brain sciences · 2025Article
- The Association of EEG μ Rhythm Phase and Power with TMS-Assessed Cortical Excitability States.Sensors (Basel, Switzerland) · 2025Article
- Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neurostimulation.Journal of neural engineering · 2025Article
- Modulation of motor excitability reflects traveling waves of neural oscillations.Cell reports · 2025Article
- Comprehensive evaluation of U-Net based transcranial magnetic stimulation electric field estimations.Scientific reports · 2025Article
- A Novel Real-time Phase Prediction Network in EEG Rhythm.Neuroscience bulletin · 2025Article
- The lab streaming layer for synchronized multimodal recording.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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- Real-time TMS-EEG for brain state-controlled research and precision treatment: a narrative review and guide.Journal of neural engineering · 2024Review
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- Method for Synthetic Generation of LFP Data for Testing of Feature Extraction AlgorithmsAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics.bioRxiv : the preprint server for biology · 2024Article
- Induced neural phase precession through exogenous electric fields.Nature communications · 2024Article
- Induced neural phase precession through exogeneous electric fields.bioRxiv : the preprint server for biology · 2023Article
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Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
objectiveReal-time approaches for transcranial magnetic stimulation (TMS) based on a specific EEG phase are a promising avenue for more precise neuromodulation interventions. However, optimal approaches to reliably extract the EEG phase in a frequency band of interest to inform TMS are still to be identified. Here, we implement a new real-time phase detection method for closed-loop EEG-TMS for robust phase extraction. We compare this algorithm with state-of-the-art methods and evaluate its performance both in silico and experimentally. APPROACH: We propose a new robust algorithm (Educated Temporal Prediction) for delivering real-time EEG phase-specific stimulation based on short prerecorded EEG training data. This method estimates the interpeak period from a training period and applies a bias correction to predict future peaks. We compare the accuracy and computation speed of the ETP algorithm with two existing methods (Fourier based, Autoregressive Prediction) using prerecorded resting EEG data and real-time experiments. MAIN
resultsWe found that Educated Temporal Prediction performs with higher accuracy than Fourier-based or Autoregressive methods both in silico and in vivo while being computationally more efficient. Further, we document the dependency of the EEG signal-to-noise ratio (SNR) on algorithm accuracy across all algorithms. SIGNIFICANCE: Our results give important insights for real-time EEG-TMS technical development as well as experimental design. Due to its robustness and computational efficiency, our method can find broad use in experimental research or clinical applications. Through open sharing of code for all three methods, we enable broad access of TMS-EEG real-time algorithms to the community.
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Registered trials
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.