Neural Signal Compression Method using Vector Sorting and SPIHT

W. Song, J.H. Choi, H.J. Ko, and T. Kim (Korea)


Neural Signal, Compression, Vector Sorting, SPIHT


Neural signals are electrical activity of neurons and are utilized in bioinformatics such as brain-computer inter face. In recent researches, multi-channel recording is com monly used to acquire neural signals, so the size of the sig nal data increases proportionally to the number of chan nels. This increase makes storing or transmission of the recorded signals more and more difficult. In this paper, we propose a new compression method for neural signals using vector alignment and sorting and the SPIHT (Set Partitioning in Hierarchical Trees) algorithm. The pro posed method maximizes the efficiency of the SPIHT by changing one-dimensional neural signals into image-like two-dimensional signals. In sorting neural signal vectors, we use two sorting criteria: peak value and cross cor relation. The compression performance of the proposed method shown by experiments to be improved on real neu ral signals as compared to the conventional SPIHT.

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