Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.

中文翻译：

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关联皮质区域的功能断开可预测 BCI 训练期间的表现

脑机接口 (BCI) 的开发主要是为了实现人类的交流、控制和神经反馈。尽管具有巨大的潜力，但 BCI 在个体之间的表现不一致，并且使人类能够实现良好控制的神经过程仍然知之甚少。为了解决这个问题，我们在涉及一组健康受试者的基于运动意象的 BCI 训练中同时进行了高密度脑电图 (EEG) 和脑磁图 (MEG) 记录。在皮层水平重建信号后，我们表明在 BCI 技能习得过程中运动相关活动的强化与在实验期间未直接针对的关联区域的逐渐断开并行。尤其，这些网络连接变化反映了与 BCI 性能和预测未来学习率相关的自动化程度不断提高。总而言之，我们的研究结果为 BCI 学习背后的大规模皮层组织机制提供了新的见解，这对在广泛的现实生活应用中改进该技术具有重要意义。