Objective. Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Approach. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interf...

中文翻译：

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脑机接口控制的学习通过大脑和行为的联合分解来证明。

客观的。基于运动想象的脑机接口（BCIs）利用个体意志调节局部大脑活动的能力，通常作为运动功能障碍的治疗方法或探索大脑活动和行为之间的因果关系。然而，许多人无法学会成功调节他们的大脑活动，这极大地限制了脑机接口的治疗和基础科学探究的功效。旨在探索 BCI 学习本质的正式实验提供了初步证据，表明跨空间分布和功能多样化的认知系统的连贯活动是能够成功学习控制 BCI 的个体的标志。然而，人们对这些分布式网络如何随着时间的推移相互作用以支持学习知之甚少。方法。在这里，我们通过构建和应用多模态网络方法来破译基于运动想象的脑机交互中的大脑行为关系来解决这一知识差距。