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中文摘要：
针对脑电图压缩效率问题,本文提出一种新的基于矩阵/张量分解的近无损多通道脑电图压缩算法。通过矩阵/张量分解模型对MC-EEG多路形式进行有效地相关分析,从而提出基于＂有损编码加上残余编码＂组成的矩阵/张量的压缩算法,对有损编码器编码分解后的残余部分进行算术编码,有效地保证了原始信号和重构信号之间的最大绝对误差。在三个不同的头皮脑电图数据集和颅内脑电图数据集上的实验验证了本文算法的有效性,实验结果表明,在同样的压缩比下,该算法比基于小波体积脑电压缩算法平均误差低了近五倍。
英文摘要：
Aiming at the problem of EEG compression efficiency, a novel near-lossless compression algorithm for multichannel electroencephalogram（MC-EEG） is proposed based on matrix/tensor decomposition models. Several matrix/tensor decomposition models are analyzed in view of efficient correlation of the multi-way forms of MC-EEG. A compression algorithm is built based on the principle of＂lossy encoding plus residual code,＂consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer,which guarantees a specifiable maximum absolute error between original and reconstructed signals. The effectiveness of proposed algorithm has been verified by experiments on three different scalp EEG datasets and an intracranial EEG dataset. Experimental results show that proposed algorithm is nearly five times the average error is lower than wavelet volume EEG under the same compression ratio.
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