A High Performance Scheme for EEG Compression Using a Multichannel Model

Gopikrishna, D.; Makur, Anamitra

doi:10.1007/3-540-36265-7_42

Cited by 10 publications

(10 citation statements)

References 13 publications

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“…In literature, one can find few multichannel compression algorithms that exploit both inter-channel and intra-channel correlations, where these correlations are often considered independent and removed using separate techniques. The existing MC-EEG compression algorithms can be categorized into lossless [7,12] and lossy [13,14] schemes. In [15], we proposed the idea of arranging the multichannel EEG in the form of a matrix or tensor, exploiting the interand intra-channel correlations by matrix/tensor decompositions, and their potential application towards compression.…”

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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“…A lossy compression technique presented in [15] achieves compression ratios of 0.1 to 0.2. Another promising method is to utilise the mutual information that exists between channels [16]. Figures 3, 4, 5 and 6 demonstrate that the challenge for the designer is to implement such algorithms at sub-milliwatt power levels.…”

Section: Possible Methods Of Data Compressionmentioning

confidence: 99%

A Key Power Trade-off in Wireless EEG Headset Design

Yates

Rodriguez‐Villegas

2007

2007 3rd International IEEE/EMBS Conference on Neural Engineering

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Abstract-The development of wireless ambulatory EEG is crucial in enabling the longer term monitoring of a patient in their everyday environment. The analysis presented here will aid the designer of a wireless EEG headset in improving the ratio of battery lifetime to battery size, with the aim of minimising the size and weight of the device. Data compression is proposed as a method to reduce the power used by the wireless transceiver, shown to dominate the system power budget. Graphs are presented which show the power available to perform varying degrees of compression in order to achieve the required lifetime or battery volume.

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“…The approach centers on a DBMS, which first facilitates storing and sharing fMRI data and eases the analysis of these data. [16] proposes a high-performance scheme for the EEG compression using a multichannel model. SignalML, a meta-format for description of biomedical time series storage, is discussed in [13].…”

Section: Introductionmentioning

confidence: 99%

Parallel Processing of Massive EEG Data with MapReduce

Wang

Chen

Ranjan

et al. 2012

2012 IEEE 18th International Conference on Parallel and Distributed Systems

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Analysis of neural signals like electroencephalogram (EEG) is one of the key technologies in detecting and diagnosing various brain disorders. As neural signals are non-stationary and non-linear in nature, it is almost impossible to understand their true physical dynamics until the recent advent of the Ensemble Empirical Mode Decomposition (EEMD) algorithm. The neural signal processing with EEMD is highly compute-intensive due to the high complexity of the EEMD algorithm. It is also dataintensive because 1) EEG signals contain massive data sets 2) EEMD has to introduce a large number of trials in processing to ensure precision. The MapReduce programming mode is a promising parallel computing paradigm for data intensive computing. To increase the efficiency and performance of the neural signal analysis, this research develops parallel EEMD neural signal processing with MapReduce. In this paper, we implement the parallel EEMD with Hadoop in a modern cyberinfrastructure. Test results and performance evaluation show that parallel EEMD can significantly improve the performance of neural signal processing.

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A High Performance Scheme for EEG Compression Using a Multichannel Model

Cited by 10 publications

References 13 publications

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

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

A Key Power Trade-off in Wireless EEG Headset Design

Parallel Processing of Massive EEG Data with MapReduce

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