论文标题
运动学习的机器学习：基于EEG的自适应康复机器人认知参与度的持续评估
Machine Learning for Motor Learning: EEG-based Continuous Assessment of Cognitive Engagement for Adaptive Rehabilitation Robots
论文作者
论文摘要
尽管认知参与度（CE）对于运动学习至关重要，但它在康复机器人中仍然不足，部分原因是它的评估目前依赖于主观和总体测量。在这里，我们提出了一个端到端的计算框架，该计算框架使用脑电图（EEG）信号作为客观测量来实时评估CE。该框架由i）一个深卷积神经网络（CNN）组成，该网络（CNN）提取任务 - 歧义时空的脑电图以预测两种类别的CE水平 - 认知参与度与脱离接触； ii）一种新型的滑动窗口方法，可实时预测CE的连续水平。我们使用内部GO/NO-GO实验评估了8个受试者的框架，该实验调整了其游戏玩法参数以引起认知疲劳。拟议中的CNN的平均保留准确性为88.13 \％。 CE预测与基于每5分钟进行一次自我报告的常用行为度量良好相关（$ρ$ = 0.93）。我们的结果使CE实时客观，并为使用CE作为康复参数的方式铺平了道路，以根据每个患者的需求和技能来调整机器人疗法。
Although cognitive engagement (CE) is crucial for motor learning, it remains underutilized in rehabilitation robots, partly because its assessment currently relies on subjective and gross measurements taken intermittently. Here, we propose an end-to-end computational framework that assesses CE in real-time, using electroencephalography (EEG) signals as objective measurements. The framework consists of i) a deep convolutional neural network (CNN) that extracts task-discriminative spatiotemporal EEG to predict the level of CE for two classes -- cognitively engaged vs. disengaged; and ii) a novel sliding window method that predicts continuous levels of CE in real-time. We evaluated our framework on 8 subjects using an in-house Go/No-Go experiment that adapted its gameplay parameters to induce cognitive fatigue. The proposed CNN had an average leave-one-out accuracy of 88.13\%. The CE prediction correlated well with a commonly used behavioral metric based on self-reports taken every 5 minutes ($ρ$=0.93). Our results objectify CE in real-time and pave the way for using CE as a rehabilitation parameter for tailoring robotic therapy to each patient's needs and skills.
