加州大学Karunesh Ganguly小组的一项最新研究研制了稳定的即插即用的脑机接口（BCI)。 相关论文于2020年9月7日发表在《自然—生物技术》杂志上。

为了开发无需重新校准即可稳定性能的方法，该团队将一个瘫痪的个体中的植入了128通道慢性皮质电图（ECoG），从而可以稳定地监测信号。研究人员证明了长期的闭环解码器适应性（其中解码器权重在几天内跨会话进行）可实现神经图和“即插即用”控件的合并。相比之下，每天重启会导致变量重新学习，从而性能退化。整合还允许在几天中添加控制特性，也就是说，长期堆积的维度。

他们的研究结果提供了一个方法，利用ECoG接口和神经可塑性的稳定性，实现可靠、稳定的BCI控制。

据悉，脑机接口可以实现严重运动障碍患者对辅助设备的控制。但由于BCI长期可靠性差以及日常校准时间太长的限制，阻碍了其在现实世界的使用。

附：英文原文

Title: Plug-and-play control of a brain–computer interface through neural map stabilization

Author: Daniel B. Silversmith, Reza Abiri, Nicholas F. Hardy, Nikhilesh Natraj, Adelyn Tu-Chan, Edward F. Chang, Karunesh Ganguly

Issue&Volume: 2020-09-07

Abstract: Brain–computer interfaces (BCIs) enable control of assistive devices in individuals with severe motor impairments. A limitation of BCIs that has hindered real-world adoption is poor long-term reliability and lengthy daily recalibration times. To develop methods that allow stable performance without recalibration, we used a 128-channel chronic electrocorticography (ECoG) implant in a paralyzed individual, which allowed stable monitoring of signals. We show that long-term closed-loop decoder adaptation, in which decoder weights are carried across sessions over multiple days, results in consolidation of a neural map and ‘plug-and-play’ control. In contrast, daily reinitialization led to degradation of performance with variable relearning. Consolidation also allowed the addition of control features over days, that is, long-term stacking of dimensions. Our results offer an approach for reliable, stable BCI control by leveraging the stability of ECoG interfaces and neural plasticity.

DOI: 10.1038/s41587-020-0662-5

Source: https://www.nature.com/articles/s41587-020-0662-5