MULTICHANNEL EEG DATA COMPRESSION

最近，记录高分辨率脑电(EEGs)从 在两个大脑中，大量的电极已经成为一个明显的趋势 研究和临床诊断。然而，目前的脑电数据 采集系统以一种从未使用过的形式存储收集的数据 自从大约30年前数字脑电出现以来，情况发生了变化。因此， 输出数据文件的大小随着 录制频道增加，导致各种问题，包括高 数据分析、数据库管理、归档和传输的成本 通过互联网。 这项建议试图通过基础研究来解决这个问题。 关于专门针对脑电数据的数据压缩，但适用于其他 生理数据也是如此。我们的主要方法是基于 应用先进的数学和信号处理技术 来解决这个关键问题。我们将开发和优化一个变量 利用样条法去除冗余数据样本的采样技术 插值法和小波变换。我们还将调查 无损数据压缩算法具有两个重要的 特点：1)压缩文件中数据的任何部分都可以读取 而不必解压缩整个文件，以及2)压缩数据 可以以粗略或精细的分辨率传输和呈现为 需要的。我们预计，使用可变抽样和无损抽样 压缩后，EEG文件大小可减少约70% 百分比。

Recently, recording high-resolution Electroencephalograms (EEGs) from a large number of electrodes has become a clear trend in both brain research and clinical diagnosis. However, the current EEG data acquisition systems store the collected data in a form that has never changed since digital EEG emerged about 30 years ago. As a result, the size of the output data file increases enormously as the number of recording channels increases, causing various problems including high costs in data analysis, database management, archiving, and transmission through the internet. This proposal seeks to solve this problem through fundamental research on data compression specifically for EEG data, but applicable to other physiological data as well. Our key approach is based on the application of advanced mathematical and signal processing technologies to this critical problem. We will develop and optimize a variable sampling technique which eliminates redundant data samples using spline interpolation and wavelet transformation. We will also investigate lossless data compression algorithms that possess two important features: 1) any part of the data within the compressed file can be read without having to decompress the entire file, and 2) the compressed data can be transmitted and presented in coarse or fine resolutions as needed. We expect that, using both variable sampling and lossless compression, the EEG file size can be reduced by approximately 70 percent.