动作电位的压缩感知研究
Study on Compressed Sensing of Action Potential
摘要：
压缩感知（CS）是一种信号处理技术，其能够实现稀疏信号的亚奈奎斯特采样和接近无损重构。该技术对于神经信号处理特别有吸引力，因为它避免了与高采样率和大数据存储相关的问题。在这个项目中，不同的CS重建算法在我们实验室记录的原始动作电位信号上进行了测试。为评价不同压缩感知算法的性能，设置了两个数值指标：压缩比（CR）和信噪比（SNR）。为了做到这一点，个人CS算法的测试平台，脑电数据的MATLAB方案内构建。该项目的主要考虑因素如下。1)字典的可行性2）对非稀疏性的容忍3）阈值或插值的适用性。
英文摘要：
Compressive sensing (CS) is a signal processing technique that enables sub-Nyquist sampling and near lossless reconstruction of a sparse signal. The technique is particularly appealing for neural signal processing since it avoids the issues relevant to high sampling rate and large data storage. In this project, different CS reconstruction algorithms were tested on raw action potential signals recorded in our lab. Two numerical criteria were set to evaluate the performance of different CS algorithms: Compression Ratio (CR) and Signal-to-Noise Ratio (SNR). In order to do this, individual CS algorithm testing platforms for the EEG data were constructed within MATLAB scheme. The main considerations for the project were the following. 1) Feasibility of the dictionary 2) Tolerance to non-sparsity 3) Applicability of thresholding or interpolation.
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