A two-dimensional approach for lossless EEG compression

Srinivasan, K.; Dauwels, Justin; Reddy, M. Ramasubba

doi:10.1016/j.bspc.2011.01.004

Cited by 66 publications

(43 citation statements)

References 38 publications

(45 reference statements)

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“…A precise control of error is achieved in each sample of the reconstructed signal, providing the desired accuracy for both automated analysis and visual inspection by clinicians. Single-channel EEG compression is widely studied, and can be categorized under lossless, near-lossless, and lossy methods (see [8] and references therein). Predictive-based coders are competitive in lossless [9] and near-lossless [10,11] scenarios, but they do not support progressive transmission and hence they are of little use in practical scenarios.…”

Section: Introductionmentioning

confidence: 99%

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

Dauwels

Srinivasan

Reddy

et al. 2013

IEEE J. Biomed. Health Inform.

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Abstract-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 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 five-fold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

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Section: Introductionmentioning

confidence: 99%

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

Dauwels

Srinivasan

Reddy

et al. 2013

IEEE J. Biomed. Health Inform.

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“…Many techniques have been developed for compressing EEG (see, e.g., [2] and references therein). However, those methods often compress individual channel separately.…”

Section: Introductionmentioning

confidence: 99%

“…From our previous studies [2,6], we found that such arrangement leads to improved compression performance over conventional vector-based compression schemes. Next, the matrices associated with the single-channel EEG signals are stacked to form 3D volume, as shown in Fig.…”

Section: Tensor/volumetric Data Formationmentioning

confidence: 99%

“…Fig 1). In the first stage, we compress I with a scalable wavelet encoder based on successive bit-plane encoding, resulting in the compressed data I en ; we use a bi-orthogonal wavelet transform (5/3 filters) as in our previous work [2]. The compressed data I en is then decoded, yielding the reconstructed data I l .…”

Section: Compression Algorithmsmentioning

confidence: 99%

“…In our previous work [2], we introduced a pre-processing technique where single-channel EEG is arranged as a matrix before compression; this representation improved the RateDistortion (R-D) performance over conventional compression schemes. Extending our previous work, in [9] we explored several ways to arrange multi-channel EEG as matrices and tensors, and evaluated several matrix/tensor decomposition techniques based on their R-D performance.…”

Section: Introductionmentioning

confidence: 99%

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Multi-channel EEG compression based on 3D decompositions

Dauwels

Srinivasan

Reddy

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Various compression algorithms for multi-channel electroencephalograms (EEG) are proposed and compared. The multichannel 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) energybased 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.

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Introduction to Multiresolution Analysis

Ouahabi

2012

Signal and Image Multiresolution Analysis

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A two-dimensional approach for lossless EEG compression

Cited by 66 publications

References 38 publications

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

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

Multi-channel EEG compression based on 3D decompositions

Introduction to Multiresolution Analysis

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