Compression schemes for EEG signals are developed based on matrix and tensor decomposition. Various ways to arrange EEG signals into matrices and tensors are explored, and several matrix and tensor decomposition schemes are applied, including SVD, CUR, PARAFAC, the Tucker decomposition, and recent random fiber selection approaches. Rate-distortion curves for the proposed matrix and tensor-based EEG compression schemes are computed. It shown that PARAFAC has the best compression performance in this context. © 2011 IEEE.

M Ramasubba Reddy

Department of Applied Mechanics

K Srinivasan

Department Of Civil Engineering

COMPRESSION PERFORMANCE

Compression scheme

EEG SIGNALS

matrix

Multi-channel

PARAFAC

RANDOM FIBERS

RATE-DISTORTION CURVES

TENSOR DECOMPOSITION

TUCKER

decomposition

signal processing

Speech communication

TENSORS

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Multi-channel EEG compression based on matrix and tensor decompositions

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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. © 2013 IEEE.

IEEE Journal of Biomedical and Health Informatics

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

Various compression algorithms for multi-channel electroencephalograms (EEG) are proposed and compared. The multi-channel EEG is represented as a three-way tensor (or 3D volume) to exploit both spatial and temporal correlations efficiently. A general two-stage coding framework is developed for multi-channel EEG compression. In the first stage, we consider (i) wavelet-based volumetric coding; (ii) energy-based lossless compression of wavelet subbands; (iii) tensor decomposition based coding. In the second stage, the residual is quantized and coded. Through such two-stage approach, one can control the maximum error (worst-case distortion). Numerical results for a standard EEG data set show that tensor-based coding achieves lower worst-case error and comparable average error than the wavelet- and energy-based schemes. © 2012 IEEE.

Multi-channel EEG compression based on 3D decompositions

An efficient preprocessing technique of arranging an electroencephalogram (EEG) signal in matrix form is proposed for real-time lossless EEG compression. The compression algorithm consists of an integer lifting wavelet transform as the decorrelator with set partitioning in hierarchical trees as the source coder. Experimental results show that the preprocessed EEG signal gave improved rate-distortion performance, especially at low bit rates, and less encoding delay compared to the conventional one-dimensional compression scheme. © 2010 The Institution of Engineering and Technology.

Electronics Letters

Efficient preprocessing technique for real-time lossless EEG compression

EURASIP Journal on Advances in Signal Processing

Compressive Sampling of EEG Signals with Finite Rate of Innovation

Linear Algebra and its Applications

Generalizing the column–row matrix decomposition to multi-way arrays

NeuroImage

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Nonnegative Matrix and Tensor Factorizations

Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN	15206149
Open Access	Yes