This paper explores the feasibility of employing EEG-based intention detection for real-time robot assistive control. We focus on predicting and distinguishing motor intentions of left/right arm movements by presenting: i) an offline data collection and training pipeline, used to train a classifier for left/right motion intention prediction, and ii) an online real-time prediction pipeline leveraging the trained classifier and integrated with an assistive robot. Central to our approach is a rich feature representation composed of the tangent space projection of time-windowed sample covariance matrices from EEG filtered signals and derivatives; allowing for a simple SVM classifier to achieve unprecedented accuracy and real-time performance. In pre-recorded real-time settings (160 Hz), a peak accuracy of 86.88% is achieved, surpassing prior works. In robot-in-the-loop settings, our system successfully detects intended motion solely from EEG data with 70% accuracy, triggering a robot to execute an assistive task. We provide a comprehensive evaluation of the proposed classifier.

翻译：本文探讨了基于脑电信号的意图检测在实时机器人辅助控制中的可行性。我们聚焦于预测和区分左/右臂运动的运动意图，具体提出：i）离线数据采集与训练流程，用于训练左右运动意图预测的分类器；ii）集成辅助机器人的在线实时预测流程，利用已训练分类器实现实时控制。本方法的核心在于构建丰富的特征表示，即通过时窗采样协方差矩阵的切空间投影，对脑电滤波信号及其导数进行处理；这使得简单的支持向量机分类器能够实现前所未有的准确率与实时性能。在预录制的实时场景（160 Hz）中，分类峰值准确率达86.88%，超越先前研究。在机器人闭环场景中，本系统仅凭脑电数据即可成功检测预期运动，准确率达70%，并触发机器人执行辅助任务。我们对所提分类器进行了全面评估。