MC-EEG Compression Based on Near-Lossless Optimized by Matrix and Tensor Decompositions
QI Xing-bi · Jiguang zazhi · 2014
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 oflossy 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.