Inria Paris, Aramis Project Team, Paris, France.
Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France.
J Neural Eng. 2021 Jan 25;18(1). doi: 10.1088/1741-2552/abc760.
Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback rehabilitation. In general, BCI usability depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modeling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from brain networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.
脑机接口（BCIs）通过解码个体的心理意图，实现与外部环境的交互。BCIs 因此可用于解决基础神经科学问题，也可解锁各种应用，从外骨骼控制到神经反馈康复。通常，BCI 的可用性取决于全面描述大脑功能和正确识别用户心理状态的能力。为此，人们投入了大量精力来改进分类算法，将局部脑活动作为输入特征。尽管取得了相当大的进展，但 BCI 的性能仍然不稳定，事实上，当前的特征只是大脑功能的过度简化描述符。在过去的十年中，越来越多的证据表明，大脑作为一个由多个专门的、空间分布的区域组成的网络系统运作，这些区域动态地整合信息。虽然更为复杂，但研究远程脑区的功能相互作用是一种更好地描述大脑功能的可行替代方法。得益于网络科学的最新进展，即一个利用图论、统计力学、数据挖掘和推理建模的现代领域，科学家们现在拥有了强大的手段来从神经影像学数据中描述复杂的大脑网络。值得注意的是，可以从大脑网络中提取摘要特征，以便在各种拓扑尺度上定量测量特定的组织属性。在这篇专题综述中，我们旨在提供最先进的支持，以发展一种网络理论方法，作为理解 BCI 和提高可用性的有前途的工具。
