A novel near-lossless compression algorithm for multi-channel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. Multi-channel EEG is represented in suitable multi-way (multi dimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multi-way forms of MC-EEG. A compression algorithm is built based on the principle of "lossy plus

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... esidual coding," consisting of a matrix/tensor decomposition based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly five-fold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.