Human-machine interactions are being increasingly explored to create alternative ways of communication and to improve our daily life. Based on a classification of the user's intention from the user's underlying neural activity, brain-computer interfaces (BCIs) allow direct interactions with the external environment while bypassing the traditional effector of the musculoskeletal system. Despite the enormous potential of BCIs, there are still a number of challenges that limit their societal impact, ranging from the correct decoding of a human's thoughts, to the application of effective learning strategies. Despite several important engineering advances, the basic neuroscience behind these challenges remains poorly explored. Indeed, BCIs involve complex dynamic changes related to neural plasticity at a diverse range of spatiotemporal scales. One promising antidote to this complexity lies in network science, which provides a natural language in which to model the organizational principles of brain architecture and function as manifest in its interconnectivity. Here, we briefly review the main limitations currently affecting BCIs, and we offer our perspective on how they can be addressed by means of network theoretic approaches. We posit that the emerging field of network neuroscience will prove to be an effective tool to unlock human-machine interactions.

中文翻译：

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网络神经科学，用于优化脑机接口。

人们正在越来越多地探索人机交互，以创建替代的通信方式并改善我们的日常生活。基于来自用户潜在的神经活动的用户意图分类，脑机接口（BCI）允许与外部环境直接交互，而绕开了肌肉骨骼系统的传统效应器。尽管BCI具有巨大的潜力，但仍然存在许多限制其BCI的社会影响的挑战，从正确地解读人的思想到应用有效的学习策略，不一而足。尽管在工程上取得了一些重要的进展，但这些挑战背后的基础神经科学仍未得到很好的探索。确实，BCI涉及在不同时空范围内与神经可塑性相关的复杂动态变化。解决这种复杂性的一种有希望的解决方法是网络科学，它提供了一种自然语言，可以用这种语言来建模大脑结构的组织原理和在其互连性中体现的功能。在这里，我们简要回顾了当前影响BCI的主要局限性，并就如何通过网络理论方法解决BCI提出了看法。我们认为，网络神经科学的新兴领域将被证明是解锁人机交互的有效工具。在这里，我们简要回顾了当前影响BCI的主要局限性，并就如何通过网络理论方法解决BCI提出了看法。我们认为，网络神经科学的新兴领域将被证明是解锁人机交互的有效工具。在这里，我们简要回顾了当前影响BCI的主要局限性，并就如何通过网络理论方法解决BCI提出了看法。我们认为，网络神经科学的新兴领域将被证明是解锁人机交互的有效工具。