局部场电位
神经解码
解码方法
计算机科学
Spike(软件开发)
脑-机接口
人工智能
神经活动
领域(数学)
神经科学
脑电图
模式识别(心理学)
心理学
软件工程
电信
数学
纯数学
作者
Andrew Jackson,Thomas M. Hall
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering
[Institute of Electrical and Electronics Engineers]
日期:2017-10-01
卷期号:25 (10): 1705-1714
被引量:55
标识
DOI:10.1109/tnsre.2016.2612001
摘要
The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain-machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.
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