Nature Biotechnology ( IF 33.1 ) Pub Date : 2020-09-07 , DOI: 10.1038/s41587-020-0662-5 Daniel B Silversmith 1, 2 , Reza Abiri 1, 2 , Nicholas F Hardy 1, 2 , Nikhilesh Natraj 1, 2 , Adelyn Tu-Chan 1, 2 , Edward F Chang 3 , Karunesh Ganguly 1, 2
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.
中文翻译:
通过神经图稳定对人机界面的即插即用控制。
脑机接口(BCI)可以控制严重运动障碍患者的辅助设备。BCI的局限性在于长期可靠性差和每天重新校准时间长,这阻碍了现实世界的采用。为了开发无需重新校准即可实现稳定性能的方法,我们在瘫痪的个体中使用了128通道的慢性皮质电图(ECoG)植入物,从而可以稳定地监视信号。我们展示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,导致了神经图和“即插即用”控制的合并。相比之下,每天重新初始化会导致性能随着可重新学习而降低。整合还允许在几天之内添加控制功能,即长期堆叠尺寸。