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Intelligent Content-Aware Model-Free Low Power Evoked Neural Signal Compression

机译:智能无内容感知的低功耗诱发神经信号压缩

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摘要

Neural recording is an important key for us to realize the neuron ac-tivity, and multi-channel recording will be more and more crucial. However, nowadays research can only deal with spontaneous signals, which characteristics are far different from evoked signals. For evoked signals, we cannot just judge the spike at the front-end because evoked signals can't be distinguished by recent spike sorting algorithm. Then, we need to send "full" waveform for bio-researchers. Therefore, proper compression algorithm is unavoidable due to full waveform transmission creates huge data amount. We use signal processing skills to get the targets for lossless compression, SNR>25db, and compression rate (compressed data / origin data)<25%.
机译:神经记录是我们实现神经元活动的重要关键,而多通道记录将变得越来越重要。但是,如今的研究只能处理自发信号,其特征与诱发信号相差甚远。对于诱发信号,我们不能仅仅判断前端的尖峰信号,因为最近的尖峰排序算法无法区分诱发信号。然后,我们需要将“完整”波形发送给生物研究人员。因此,由于全波形传输会产生巨大的数据量,因此不可避免的是采用适当的压缩算法。我们使用信号处理技能来获得无损压缩,SNR> 25db和压缩率(压缩数据/原始数据)<25%的目标。

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