In this paper, we study various lossless compression techniques for electroencephalograph (EEG) signals. We discuss a computationally simple pre-processing technique, where EEG signal is arranged in the form of a matrix (2-D) before compression. We discuss a two-stage coder to compress the EEG matrix, with a lossy coding layer (SPIHT) and residual coding layer (arithmetic coding). This coder is optimally tuned to utilize the source memory and the i.i.d. nature of the residual. We also investigate and compare EEG compression with other schemes such as JPEG2000 image compression standard, predictive coding based shorten, and simple entropy coding. The compression algorithms are tested with University of Bonn database and Physiobank Motor/Mental Imagery database. 2-D based compression schemes yielded higher lossless compression compared to the standard vector-based compression, predictive and entropy coding schemes. The use of pre-processing technique resulted in 6% improvement, and the two-stage coder yielded a further improvement of 3% in compression performance. © 2011 Elsevier Ltd. All rights reserved.

M Ramasubba Reddy

Department of Applied Mechanics

K Srinivasan

Department Of Civil Engineering

ARITHMETIC CODING

Correlation coefficient

Electroencephalogram (EEG)

JPEG 2000

Relative energies

SPIHT

DIGITAL IMAGE STORAGE

entropy

Image coding

Image compression

Electroencephalography

Algorithm

article

Coding

Compression

controlled study

electroencephalogram

Image analysis

priority journal

signal processing

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

Biomedical Signal Processing and Control

In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals are presented based on image and volumetric coding. Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated across time. Suitable representations are proposed to utilize those correlations effectively. In particular, multichannel EEG is represented either in the form of image (matrix) or volumetric data (tensor), next a wavelet transform is applied to those EEG representations. The compression algorithms are designed following the principle of "lossy plus residual coding," consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual. Such approach guarantees a specifiable maximum error between original and reconstructed signals. The compression algorithms are applied to three different EEG datasets, each with different sampling rate and resolution. The proposed multichannel compression algorithms achieve attractive compression ratios compared to algorithms that compress individual channels separately. © 2012 IEEE.

Fulltext

IEEE Journal of Biomedical and Health Informatics

Multichannel EEG compression: Wavelet-based image and volumetric coding approach

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.

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.

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

Multi-channel EEG compression based on 3D decompositions

Medical studies have shown that EEG of Alzheimer's disease (AD) patients is "slower" (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83 (MCI) and 98 (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone. Copyright © 2011 Justin Dauwels et al.

International Journal of Alzheimer's Disease

Slowing and loss of complexity in Alzheimer's EEG: Two sides of the same coin?

Widespread use of Multichannel Electroencephalograph (MCEEG) in diversified fields ranging from clinical studies to Brain Computer Interface (BCI) application, has put in a lot of thrust in data processing concepts, for effective storage and transmission. The paper proposes a computationally simple and novel methodology Normalized Spatial Pseudo Codec (n-SPC) to compress MCEEG signals. The signals are first normalized followed by two operations namely the spatial coding and pseudo coding operating on integer part and fractional part of the normalized data respectively. The proposed method was evaluated on publicly available EEG databases and results indicate that the algorithm exhibits good storage efficiency with average Compression Ratio (CR) of 4.61 with a computational complexity of only O(zN). The algorithm offers significantly a better decompressed signal quality, quantified by average Peak Signal to Noise Ratio (PSNR) of 21.42 dB. The average encoding and decoding time per sample is 0.3 and 0.04 ms, respectively with an average Percentage Root Mean Square Deviation (PRD) of 5.33. The efficacy was further evaluated using the decompressed signal to detect sleep spindle, from an excerpt of EEG recording and was compared with the visual scoring of two experts, available at the DREAMS Sleep Spindles Database. Hence, the proposed compression scheme can be used in practical MCEEG recording, archiving and BCI and neuromorphic systems. © 2020, © 2020 IETE.

IETE Journal of Research

A Simple but Efficient EEG Data Compression Algorithm for Neuromorphic Applications

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.

Journal	Biomedical Signal Processing and Control
ISSN	17468094
Open Access	No