Rapid adaptation of brain-computer interfaces to new neuronal ensembles or participants via generative modelling.

Rapid adaptation of brain-computer interfaces to new neuronal ensembles or participants via generative modelling.
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DOI:
10.1038/s41551-021-00811-z
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发表时间:
2023-04
影响因子:
28.1
通讯作者:
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中科院分区:
工程技术1区
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对于脑机接口(BCI),获得足够的训练数据来将神经信号映射到动作上的算法可能是困难的,昂贵的,甚至是不可能的。在这里,我们报告了一个生成模型的开发和使用-一个模型,从学习的数据分布合成了几乎无限数量的新数据分布-学习手部运动学和相关的神经尖峰序列之间的映射。生成尖峰训练合成器是在来自一个记录会话的数据上训练的,其中猴子执行一个到达任务,并且可以通过使用有限的额外神经数据来快速适应新的会话或猴子。我们表明,该模型可以适应合成新的尖峰列车,加速训练和提高BCI解码器的泛化。该方法是完全数据驱动的,因此适用于电机控制之外的BCI应用。关于研究设计的进一步信息可在与本文链接的《自然研究报告摘要》中找到。
For brain–computer interfaces (BCIs), obtaining sufficient training data for algorithms that map neural signals onto actions can be difficult, expensive or even impossible. Here, we report the development and use of a generative model — a model that synthesizes a virtually unlimited number of new data distributions from a learned data distribution — that learns mappings between hand kinematics and the associated neural spike trains. The generative spike-train synthesizer is trained on data from one recording session with a monkey performing a reaching task, and can be rapidly adapted to new sessions or monkeys by using limited additional neural data. We show that the model can be adapted to synthesize new spike trains, accelerating the training and improving the generalization of BCI decoders. The approach is fully data-driven, and hence applicable to applications of BCIs beyond motor control. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
DOI: 10.1038/s41551-017-0169-7
发表时间: 2017-12
影响因子: 28.1
作者:
Dyer EL;Gheshlaghi Azar M;Perich MG;Fernandes HL;Naufel S;Miller LE;Körding KP
通讯作者: Körding KP
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