A 1.5-D Multi-Channel EEG Compression Algorithm Based on NLSPIHT

This letter proposes a novel 1.5-D algorithm for multi-channel electroencephalogram (EEG) compression. The proposed algorithm only needs to perform 1-D Discrete Wavelet Transform (DWT) rather than the 2-D version employed by previous works, and thus it results in lower computational complexity and power dissipation. In this algorithm, a new 2-D arranging method that exploits correlations between different sub-bands is developed to concentrate the energy, which causes more efficient compression using No List Set Partitioning in Hierarchical Trees (NLSPIHT) algorithm. Experimental results demonstrate that the proposed algorithm outperforms 2-D NLSPIHT algorithm under the same compression ratio (CR) and it is slightly inferior to 2-D SPIHT algorithm in the near-lossless compression regime, but it can provide a better fidelity with respect to higher CRs.