Müller-Putz Gernot R, Kobler Reinmar J, Pereira Joana, Lopes-Dias Catarina, Hehenberger Lea, Mondini Valeria, Martínez-Cagigal Víctor, Srisrisawang Nitikorn, Pulferer Hannah, Batistić Luka, Sburlea Andreea I
Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
BioTechMed, Graz, Austria.
Front Hum Neurosci. 2022 Mar 11;16:841312. doi: 10.3389/fnhum.2022.841312. eCollection 2022.
Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project "Feel Your Reach". In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram (EEG). In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the "Feel Your Reach" framework to people with cervical spinal cord injury and evaluate the decoders' performance while participants to perform upper-limb movements. While on the one hand, we made major progress towards this ambitious goal, we also critically discuss current limitations.
建立一个用于持续解码手部/手臂运动意图的框架的基础知识、方法和技术,是由欧洲研究理事会资助的“感受你的触及范围”项目的目标。在这项工作中,我们回顾了过去6年中我们进行和实施的研究及方法,这些研究和方法为使严重瘫痪的人能够从脑电图(EEG)实时无创控制机器人手臂奠定了基础。详细而言,我们研究了目标导向运动检测、已执行和尝试的运动轨迹解码、抓握关联、错误处理以及动觉反馈。尽管我们已经在目标人群中测试了一些方法,但我们仍需将“感受你的触及范围”框架应用于颈脊髓损伤患者,并在参与者进行上肢运动时评估解码器的性能。一方面,我们朝着这个宏伟目标取得了重大进展,同时我们也批判性地讨论了当前的局限性。