Objective. Error-related potentials (ErrPs) are elicited in the human brain following an error’s perception. Recently, ErrPs have been observed in a novel task situation, i.e. when stroke patients perform upper-limb rehabilitation exercises. These ErrPs can be used to develop assist-as-needed (AAN) robotic stroke rehabilitation systems. However, to date, there is no reported research on assessing the feasibility of using the ErrPs to implement the AAN approach. Hence, in this study, we evaluated and compared the single-trial classification of novel ErrPs using various classical machine learning and deep learning approaches. Approach. Electroencephalogram data of 13 stroke patients recorded while performing an upper-limb physical rehabilitation exercise were used. Two classification approaches, one combining the xDAWN spatial filtering and support vector machines, and the other using a convolutional neural network-based double transfer learning, were utilized. Main results. Results showed that the ErrPs could be detected with a mean area under the receiver operating characteristics curve of 0.838, and a mean accuracy of 0.842, 0.257 above the chance level (p < 0.05), for a within-subject classification. The results indicated the feasibility of using ErrP signals in real-time AAN robot therapy with evidence from the conducted latency analysis, cross-subject classification, and three-class asynchronous classification. Significance. The findings presented support our proposed approach of using ErrPs as a measure to trigger and/or modulate as required the robotic assistance in a real-time human-in-the-loop robotic stroke rehabilitation system.

客观的。错误相关电位 (ErrPs) 是在错误感知之后在人脑中引发的。最近，在一种新的任务情况下观察到 ErrP，即中风患者进行上肢康复锻炼时。这些 ErrP 可用于开发按需辅助(AAN) 机器人中风康复系统。然而，迄今为止，还没有关于评估使用 ErrPs 实施 AAN 方法的可行性的研究报告。因此，在本研究中，我们使用各种经典机器学习和深度学习方法评估和比较了新型 ErrP 的单次试验分类。方法。使用了 13 名中风患者在进行上肢康复锻炼时记录的脑电图数据。使用了两种分类方法，一种结合 xDAWN 空间过滤和支持向量机，另一种使用基于卷积神经网络的双转移学习。主要结果。结果表明，可以检测到 ErrPs，接受者操作特征曲线下的平均面积为 0.838，平均准确度为 0.842，高于机会水平 0.257（p< 0.05)，用于受试者内分类。结果表明在实时 AAN 机器人治疗中使用 ErrP 信号的可行性，并有来自进行的延迟分析、跨学科分类和三类异步分类的证据。意义。提出的研究结果支持我们提出的方法，即使用 ErrPs 作为一种措施，根据需要在实时人机回路机器人中风康复系统中触发和/或调节机器人辅助。