Theoretical and Experimental Investigation of an Efficient SVD-based Near-lossless Compression Algorithm for Multichannel EEG Signals

In this paper, we investigate performance of a re-cently proposed near-lossless compression algorithm specifically devised for multichannel electroencephalograph (EEG) signals. The algorithm exploits the fact that singular value decomposition (SVD) is usually performed on EEG signals for denoising and removing unwanted artifacts and that the same SVD can be used for compression purpose. In this paper, we derived an analytical expression for the expected compression ratio and an upper bound for the maximum distortion introduced by the algorithm after reconstruction. Moreover, performances of the algorithm have been investigated on an extended dataset containing real EEG signals related to subjects performing different sensorimotor tasks. Both analytical and experimental results reported in this paper show that the algorithm is able to attain a compression ratio proportional to the number of EEG channels by achieving a percentage root mean square distortion (PRD) in the order of 0.01 %. In particular, the achieved PRD is very low if compared with other state-of-the-art compression algorithms with similar complexity. Moreover, the algorithm allows the desired maximum absolute error to be fixed a priori. Therefore, we can consider this algorithm as an efficient tool for reducing the amount of memory necessary to record data and, at the same time, preserving actual clinical information of the signals besides compression.