Chatterjee Tanaya, Guzzo Adrien, Tlaie Alejandro, Kaddour Ahmad, Papaxanthis Charalambos, Gaveau Jeremie, Dominey Peter Ford
Université Bourgogne Europe, INSERM, CAPS UMR 1093, 21000 Dijon, France.
GIS STARTER, Dijon, France.
iScience. 2025 Jun 18;28(7):112914. doi: 10.1016/j.isci.2025.112914. eCollection 2025 Jul 18.
A large proportion of human behavior is organized in time in the form of sensorimotor sequences. Learning new behavioral sequences recruits cognitive functions with their neural underpinnings. Here, we characterize how neurophysiological activity revealed in the EEG signal can reflect these behavioral processes. This was investigated in a face-to-face human-robot interaction, where the robot demonstrated a continuous pointing sequence, which the human mimicked. We observed task-related modulation of the event-related spectral perturbations (ERSP) in distinct ways for rest, fixation, and movement sequences. We also observed modulation of the ERSP by the motor sequence learning. Using a Markov-switching linear regression model, we further demonstrated that the EEG signal could be used to decode the human and robot movements. These results are significant both in the context of neural coding of motor performance and learning, as well as in the context of neural coding of joint action, in the face-to-face human-robot interaction.
很大一部分人类行为是以感觉运动序列的形式在时间上组织起来的。学习新的行为序列会调动具有神经基础的认知功能。在这里，我们描述了脑电图（EEG）信号中揭示的神经生理活动如何能够反映这些行为过程。这是在面对面的人机交互中进行研究的，其中机器人展示了一个连续的指向序列，人类进行模仿。我们观察到，对于休息、注视和运动序列，事件相关频谱扰动（ERSP）以不同方式出现与任务相关的调制。我们还观察到运动序列学习对ERSP的调制。使用马尔可夫切换线性回归模型，我们进一步证明EEG信号可用于解码人类和机器人的动作。这些结果在运动表现和学习的神经编码背景下，以及在面对面人机交互中的联合动作神经编码背景下都具有重要意义。
