Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity.,Nature Biomedical Engineering

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Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity.
Nature Biomedical Engineering ( IF 26.8 ) Pub Date : 2020-04-20 , DOI: 10.1038/s41551-020-0542-9
Alan D Degenhart _{1,

2,

3,

4,

5} , William E Bishop _{3,

6,

7} , Emily R Oby _{2,

3,

4,

5,

8} , Elizabeth C Tyler-Kabara _{2,

9,

10,

11} , Steven M Chase _{3,

12,

13} , Aaron P Batista _{2,

3,

4,

5} , Byron M Yu _{1,

3,

12,

13}

Affiliation

Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
Brain Institute, University of Pittsburgh, Pittsburgh, PA, USA.
Systems Neuroscience Center, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
Department of Neurobiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.

The instability of neural recordings can render clinical brain-computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs.

中文翻译：

通过对齐神经活动的低维空间来稳定脑机接口。

神经记录的不稳定性会使临床脑机接口 (BCI) 无法控制。在这里，我们表明低维神经流形（描述神经元之间特定相关模式的低维空间）的对齐可用于稳定神经活动，从而在存在记录不稳定性的情况下保持 BCI 性能。在存在严重和突然的记录不稳定性的情况下，我们通过皮质内 BCI 在在线光标控制期间评估了非人类灵长类动物的稳定剂。稳定的 BCI 在不同的不稳定条件下和多天后恢复了熟练的控制。稳定器不需要了解用户意图，并且可以胜过有监督的重新校准。即使神经活动包含关于光标移动方向的少量信息，它也能稳定 BCI。该稳定剂可能适用于其他神经接口，并可能提高 BCI 的临床生存能力。

更新日期：2020-04-24

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