Martinez-Cagigal Victor, Kobler Reinmar J, Mondini Valeria, Hornero Roberto, Muller-Putz Gernot R
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2981-2985. doi: 10.1109/EMBC44109.2020.9175723.
Decoding upper-limb movements in invasive recordings has become a reality, but neural tuning in non-invasive low-frequency recordings is still under discussion. Recent studies managed to decode movement positions and velocities using linear decoders, even developing an online system. The decoded signals, however, exhibited smaller amplitudes than actual movements, affecting feedback and user experience. Recently, we showed that a non-linear offline decoder can combine directional (e.g., velocity) and non-directional (e.g., speed) information. In this study, it is assessed if the non-linear decoder can be used online to provide real-time feedback. Five healthy subjects were asked to track a moving target by controlling a robotic arm. Initially, the robot was controlled by their right hand; then, the control was gradually switched until it was entirely controlled by the electroencephalogram (EEG). Correlations between actual and decoded movements were generally above chance level. Results suggest that information about speed was also encoded in the EEG, demonstrating that the proposed non-linear decoder is suitable for decoding real-time arm movements.
在侵入性记录中解码上肢运动已成为现实，但非侵入性低频记录中的神经调谐仍在讨论之中。最近的研究设法使用线性解码器解码运动位置和速度，甚至开发了一个在线系统。然而，解码信号的幅度比实际运动小，影响了反馈和用户体验。最近，我们表明非线性离线解码器可以结合方向（如速度）和非方向（如速率）信息。在本研究中，评估了非线性解码器是否可用于在线提供实时反馈。五名健康受试者被要求通过控制机械臂跟踪移动目标。最初，机器人由他们的右手控制；然后，控制逐渐切换，直到完全由脑电图（EEG）控制。实际运动与解码运动之间的相关性通常高于机遇水平。结果表明，关于速率的信息也编码在脑电图中，这表明所提出的非线性解码器适用于解码实时手臂运动。
