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.

中文翻译：

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变更点检测器集成：对实时BMI应用的影响。

脑机接口（BMI）已被广泛用于研究基本和翻译神经科学问题。在实时闭环神经科学实验中，出现了许多实际问题，例如逐次试验的变异性，尖峰分类噪声或多单元活动。在本文中，我们提出了一种在BMI应用于检测急性疼痛信号的背景下，基于独立检测器集合的变化点检测新框架。出于整体学习的目的，我们提出的“变化点检测器集合”（ECPD）集成了来自独立检测器的多个决策，这些决策可以基于不同试验记录的数据，不同大脑区域记录的数据，不同方式的数据或来自不同学习方法的模型。通过整合多种信息来源，ECPD旨在提高检测准确度（以真实的阳性率和真实的阴性率表示），并在灵敏度和特异性之间取得最佳平衡。我们使用行为自由的大鼠的计算机模拟和实验记录验证了我们的方法。我们的结果表明，ECPDS在基于从单个或多个大脑区域记录的神经元群体尖峰活动（或结合局部场电势）来检测急性疼痛信号的发作时，具有优越而强大的性能。