Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.

中文翻译：

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运动神经影像任务中神经生理指标评估的回归网络

运动影像（MI）促进活动中的运动学习，例如发展专业的运动技能，运动手势和患者康复。但是，由于受试者之间和受试者内部的差异性，多达30％的用户在训练课程后可能无法培养足够的协调技能。在这里，我们开发了一个数据驱动的估计器，称为深度回归网络（DRN），该估计器联合提取并执行回归分析，以评估各个脑部网络在执行MI任务中的效率。所提出的双阶段估计器最初学习从输入数据中提取的一组深模式，以便提供神经回归模型，从而可以推断具有相似可变性的主题程序集之间的区别。这些结果是根据真实世界的MI数据获得的，