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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Acknowledgements
The authors thank Eric J. Robinson for English proofreading. The work was supported by the NSF-CRCNS grant IIS-130764 (Z.C.), NSF-NCS grant #1835000 (Z.C., J.W.), NIH grants R01-NS100016 (Z.C., J.W.), R01-MH118928 (Z.C.) and R01-GM115384 (J.W.), as well as the China’s Natural Science Foundation #31627802 and Fundamental Research Funds for the Central Universities (Y.C.).
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Xiao, Z., Hu, S., Zhang, Q. et al. Ensembles of change-point detectors: implications for real-time BMI applications. J Comput Neurosci 46, 107–124 (2019). https://doi.org/10.1007/s10827-018-0694-8
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DOI: https://doi.org/10.1007/s10827-018-0694-8