Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions

• Published in: IEEE journal of biomedical and health informatics Vol. 17; no. 3; pp. 708 - 714
• Main Authors: Dauwels, J., Srinivasan, K., Reddy, M. R., Cichocki, A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arithmetic coding; compression; Compression algorithms; Correlation; Data compression; Decomposition; Distortion measurement; electroencephalogram (EEG); Electroencephalography; Electroencephalography - instrumentation; Electroencephalography - methods; Encoding; Humans; Matrix decomposition; multichannel EEG; parallel factor decomposition (PARAFAC); Signal Processing, Computer-Assisted; singular value decomposition (SVD); Tensile stress

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 mat...