ArticleJournal of computational neuroscience2019
Ensembles of change-point detectors: implications for real-time BMI applications.
Article in Journal of computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Factorized binary search: Change point detection in the network structure of multivariate high-dimensional time series.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- The decoder design and performance comparative analysis for closed-loop brain-machine interface system.Cognitive neurodynamics · 2024Article
- A prototype closed-loop brain-machine interface for the study and treatment of pain.Nature biomedical engineering · 2023Article
- Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents.Science translational medicine · 2022Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
- Pharmacological restoration of anti-nociceptive functions in the prefrontal cortex relieves chronic pain.Progress in neurobiology · 2021Article
- Detecting acute pain signals from human EEG.Journal of neuroscience methods · 2021Article
- Granger causality analysis of rat cortical functional connectivity in pain.Journal of neural engineering · 2020Article
- A Predictive Coding Model for Evoked and Spontaneous Pain Perception.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- Emerging techniques in statistical analysis of neural data.Journal of computational neuroscience · 2019Article
- Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex.Frontiers in cellular neuroscience · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Brain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed "ensembles of change-point detectors" (ECPDs) integrate multiple decisions from independent detectors, which may be derived based on data recorded from different trials, data recorded from different brain regions, data of different modalities, or models derived from different learning methods. By integrating multiple sources of information, the ECPDs aim to improve detection accuracy (in terms of true positive and true negative rates) and achieve an optimal trade-off of sensitivity and specificity. We validate our method using computer simulations and experimental recordings from freely behaving rats. Our results have shown superior and robust performance of ECPDS in detecting the onset of acute pain signals based on neuronal population spike activity (or combined with local field potentials) recorded from single or multiple brain regions.
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