Detection of movement intention using EEG in a human-robot interaction environment

Introduction : This paper presents a detection method for upper limb movement intention as part of a brain-machine interface using EEG signals, whose final goal is to assist disabled or vulnerable people with activities of daily living. Methods EEG signals were recorded from six naïve healthy volunteers while performing a motor task. Every volunteer remained in an acoustically isolated recording room. The robot was placed in front of the volunteers such that it seemed to be a mirror of their right arm, emulating a Brain Machine Interface environment. The volunteers were seated in an armchair throughout the experiment, outside the reaching area of the robot to guarantee safety. Three conditions are studied: observation, execution, and imagery of right arm’s flexion and extension movements paced by an anthropomorphic manipulator robot. The detector of movement intention uses the spectral F test for discrimination of conditions and uses as feature the desynchronization patterns found on the volunteers. Using a detector provides an objective method to acknowledge for the occurrence of movement intention. Results When using four realizations of the task, detection rates ranging from 53 to 97% were found in five of the volunteers when the movement was executed, in three of them when the movement was imagined, and in two of them when the movement was observed. Conclusions Detection rates for movement observation raises the question of how the visual feedback may affect the performance of a working brain-machine interface, posing another challenge for the upcoming interface implementation. Future developments will focus on the improvement of feature extraction and detection accuracy for movement intention using EEG data.引言：本文提出一种基于脑电（Electroencephalogram, EEG）信号的脑机接口（Brain-Machine Interface, BMI）上肢运动意图检测方法，其最终目标是辅助残障或弱势人群开展日常生活活动。 方法：招募6名无相关实验经验的健康志愿者，在其执行运动任务时采集脑电信号。所有志愿者均在隔音记录室内完成实验。将机器人放置于志愿者前方，使其视觉效果等效于志愿者右臂的镜像视图，以此搭建脑机接口实验环境。实验全程志愿者均坐在扶手椅上，且处于机器人的作业范围之外以保障安全。本研究设置三种实验条件：由拟人化操作机器人控制节奏的右臂屈伸运动的观察、执行与想象任务。本运动意图检测器采用谱F检验对三类实验条件进行区分，并以志愿者脑电信号中提取的去同步化模式作为分类特征。该检测器可提供一种客观方法来识别运动意图的发生。 结果：当采用4次任务重复数据进行分析时，5名志愿者在运动执行条件下的检测准确率介于53%至97%之间；3名志愿者在运动想象条件下达到该精度范围；2名志愿者在运动观察条件下获得了该区间的检测结果。 结论：运动观察条件下的检测准确率结果引发了一个研究疑问：视觉反馈可能会对实用脑机接口的性能产生何种影响，这为后续脑机接口的落地实施带来了新的挑战。未来的研究将聚焦于优化基于脑电数据的运动意图特征提取方法与检测精度。