基于分割矢量量化的神经脉冲压缩植入式bmi

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI:10.1109/IranianCEE.2019.8786761

Nazanin Ahmadi Dastgerdi, Hossein Hoseini-Nejad, H. Amiri

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引用次数: 0

摘要

本文报道了一种基于分裂矢量量化(SVQ)的可植入皮质内神经记录微系统的脉冲压缩方法。该方法的峰值压缩比为14.8，但代价是分类精度(CA)降低。在神经信号的信噪比(SNR)的宽范围内(7 ~ 15)，CA的平均值为94%。

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Neural Spike Compression Based on Split Vector Quantization for Implantable BMIs

This paper reports a novel spike compression approach for implantable intra-cortical neural recording microsystems based on Split Vector Quantization (SVQ). The proposed method presents a spike compression ratio 14.8 at the cost of classification accuracy (CA). The average value of CA is 94% over a wide range (7 to 15) of signal to noise ratios (SNR) of the neural signal.

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来源期刊

2019 27th Iranian Conference on Electrical Engineering (ICEE)

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