Near-lossless multichannel EEG compression based on matrix and tensor decompositions

Justin Dauwels, K. Srinivasan, M. Ramasubba Reddy, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of "lossy plus residual 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 fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

Original languageEnglish
Pages (from-to)708-714
Number of pages7
JournalIEEE Journal of Biomedical and Health Informatics
Issue number3
Publication statusPublished - 2013
Externally publishedYes


  • Arithmetic coding
  • Compression
  • Electroencephalogram (EEG)
  • Multichannel EEG
  • Parallel factor decomposition (PARAFAC)
  • Singular value decomposition (SVD)


Dive into the research topics of 'Near-lossless multichannel EEG compression based on matrix and tensor decompositions'. Together they form a unique fingerprint.

Cite this