Lossless compression of electroencephalographic (EEG) data

Magotra, N.; Mandyam, Giridhar; Sun, Mingui; McCoy, W.

doi:10.1109/iscas.1996.541709

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…Lossless predictive schemes directly code the residuals, whereas lossy predictive schemes threshold and quantize the residuals to improve compression rate (at the cost of increased error). Various prediction models have been developed: this include linear AR model [3,4], recursive-least-squares predictor [5], adaptive neural networks [6] and models based on chaos theory [7]. Refinements such as context-based bias cancellation [4], and adaptive error modeling schemes [8,9] further improve the performance.…”

Section: Introductionmentioning

confidence: 99%

A two-dimensional approach for lossless EEG compression

Srinivasan

Dauwels

Reddy

2011

Biomedical Signal Processing and Control

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Section: Introductionmentioning

confidence: 99%

A two-dimensional approach for lossless EEG compression

Srinivasan

Dauwels

Reddy

2011

Biomedical Signal Processing and Control

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Lossless compression of electromyographic signal

Chanasabaeng

Charoen

Paphangkorakit

2012

The 5th 2012 Biomedical Engineering International Conference

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Neural Network Based Near- Lossless Compression of EEG Signals with Non Uniform Quantization

Sriraam¹

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Efficient compression technique is highly essential for the transmission and storage of large amount of biomedical signals. In this paper, a near- lossless scheme for the compression of EEG signals using artificial neural networks is proposed. The error (residue) signals which is obtained due to the difference between the original and the predicted EEG signals are thresolded based on a term referred as absolute error limit (AEL) such that, any error samples above the limit require more number of bits than the samples below the limit that require less number of bits. The thresholded error samples are quantized in a non-uniform manner by varying the actual bits assigned to the error samples. An arithmetic encoder is further used to improve the compression efficiency. Three adaptive neural network models, namely, single and multilayer perceptrons and Elman neural network and two classical adaptive predictors such as autoregressive model(AR) and normalized least mean-square FIR filter are used. EEG signals recorded under different physiological conditions are considered and the performance of the proposed scheme is evaluated in terms of compression ratio and the fidelity parameter, percent of root-mean-square-difference (PRD). It is found from the experimental results that the variation of error limit and quantization step decides the overall compression performance. Single- layer perceptron yields the best compression results in terms of utilizing less bit rate as well achieving low PRD values compared to other predictors.

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Lossless compression of electroencephalographic (EEG) data

Cited by 6 publications

References 4 publications

A two-dimensional approach for lossless EEG compression

A two-dimensional approach for lossless EEG compression

Lossless compression of electromyographic signal

Neural Network Based Near- Lossless Compression of EEG Signals with Non Uniform Quantization

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