Exploiting correlation in neural signals for data compression
Exploiting correlation in neural signals for data compression
复制标题
利用神经信号的相关性进行数据压缩
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Paul
中科院分区：
文献类型：
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作者：
Sebastian Schmale;J. Hoeffmann;Benjamin Knoop;G. Kreiselmeyer;H. Hamer;D. Peters;S. Paul
侵入性脑研究的进展依赖于高时间和空间分辨率的信号采集，导致与外部世界的（无线）接口数据泛滥。因此，在植入部位进行数据压缩是必要的，以符合神经生理学限制，特别是在记录和传输神经原始数据时。这项工作研究神经信号的空间相关性，导致在植入部位的无线数据传输之前，使用合适的稀疏信号表示的数据压缩显著增加。随后，我们使用了相关意识的二维DCT用于图像处理，利用空间相关的数据集。为了保证一定的稀疏性的信号表示，迫零的两个范例进行评估和应用：重要系数-和块稀疏迫零。
Progress in invasive brain research relies on signal acquisition at high temporal- and spatial resolutions, resulting in a data deluge at the (wireless) interface to the external world. Hence, data compression at the implant site is necessary in order to comply with the neurophysiological restrictions, especially when it comes to recording and transmission of neural raw data. This work investigates spatial correlations of neural signals, leading to a significant increase in data compression with a suitable sparse signal representation before the wireless data transmission at the implant site. Subsequently, we used the correlation-aware two-dimensional DCT used in image processing, to exploit spatial correlation of the data set. In order to guarantee a certain sparsity in the signal representation, two paradigms of zero forcing are evaluated and applied: Significant coefficients- and block sparsity-zero forcing.