scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Multichannel EEG Compression: Wavelet-Based Image and Volumetric Coding Approach

TL;DR: In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals are presented based on image and volumetric coding, consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual.
Abstract: 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.

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI
01 Jul 2016
TL;DR: The theory of wavelet denoising method, common spatial pattern algorithm and linear discriminant analysis algorithm are investigated and the effectiveness and accuracy of these algorithms on EEG signalDenoising, feature extraction, and classification are demonstrated.
Abstract: To increase the performance of a brain–computer interface and brain–machine interface system, we propose some methods and algorithms for electroencephalograph (EEG) signal analysis. The recorded EEG signal is transmitted to the computer and the upper limb robotic arm interface via a bluetooth. To obtain effective commands from brain, the recorded EEG signal is processed by a front filter, denoise filter, feature extraction, and classification, while the personal computer software and upper limb arm are driven by EEG-based commands. Through the encoders and gyroscopes on the upper limb arm, we can acquire some feedback signals in real time, such as joint angle, arm accelerated speed, and angular speed. The theory of wavelet denoising method, common spatial pattern algorithm and linear discriminant analysis algorithm are investigated in this paper. The simulations and experiments demonstrate the effectiveness and accuracy of these algorithms on EEG signal denoising, feature extraction, and classification.

59 citations


Cites methods from "Multichannel EEG Compression: Wavel..."

  • ...[26] used a wavelet-based image and volumetric coding to achieve...

    [...]

Journal ArticleDOI
TL;DR: A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) based on matrix/tensor decomposition models that achieves attractive compression ratios compared to compressing individual channels separately.
Abstract: 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.

51 citations


Cites background or methods from "Multichannel EEG Compression: Wavel..."

  • ...In similar to the coders in [16], they support progressive quality and guarantee the maximum distortion bound (eq....

    [...]

  • ...We consider two ways to form a three-way tensor from MC-EEG [16]....

    [...]

  • ...In the residual coding stage, if the symbol size M of the residual is small (M ≤ 128), then residual is arithmetic coded directly, else the residual stream is split into sub streams of smaller symbol size and each stream is arithmetic coded separately [16, 20]....

    [...]

  • ...4) with wavelet-based singlechannel [8] and image/volumetric coding algorithms [16]....

    [...]

  • ...Further, in [16], we developed compression algorithms using image/volumetric wavelet coders that supports progressive quality, progressive resolution, and guarantee a maximum distortion bound in L∞ sense (cf....

    [...]

Journal ArticleDOI
TL;DR: The altered compressibility of EEG with CS can act as a good biomarker for distinguish seizure-free, per-seizure and seizure state and enables tele-monitoring of epilepsy patients using wireless body-area networks in personalized medicine.

49 citations


Cites methods from "Multichannel EEG Compression: Wavel..."

  • ...thresholding-based DWT compression algorithm is adopted [32], which just retains the significant wavelet coefficients above some presetting threshold to reconstruct the original signal....

    [...]

Journal ArticleDOI
15 Jan 2014-Sensors
TL;DR: This paper proposes the use of a compressed sensing (CS) framework to efficiently compress EEG signals at the sensor node and shows that this framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission.
Abstract: The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.

47 citations

Posted Content
TL;DR: In this article, an optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low rank structures in the reconstructed multi-channel EEG signals.
Abstract: Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low rank structures in the reconstructed multi-channel EEG signals. Both convex relaxation and global consensus optimization with alternating direction method of multipliers are used to compute the optimization model. Results: The performance of multi-channel EEG signal reconstruction is improved in term of both accuracy and computational complexity. Conclusion: The proposed method is a better candidate than previous sparse signal recovery methods for compressed sensing of EEG signals. Significance: The proposed method enables successful compressed sensing of EEG signals even when the signals have no good sparse representation. Using compressed sensing would much reduce the power consumption of wireless EEG system.

40 citations

References
More filters
Journal ArticleDOI
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...

11,407 citations

01 Jan 1998
TL;DR: Historical aspects introduction to the neurophysiological basis of the EEG and DC potentials cellular substrates of spontaneous and evoked brain rhythms dynamics of EEG as signals and neuronal populations are introduced.
Abstract: Historical aspects introduction to the neurophysiological basis of the EEG and DC potentials cellular substrates of spontaneous and evoked brain rhythms dynamics of EEG as signals and neuronal populations - models and theoretical considerations biophysical aspects of EEG and magnetoencephalogram generation technological basis of the EEG recording EEG recording and operation of the apparatus the EEG signal - polarity and field determination digitized (paperless) EEG recording the normal EEG in the waking adult sleep and EEG maturation of the EEG - development of waking and sleep patterns EEG patterns and genetics nonspecific abnormal EEG patterns abnormal EEG patterns - epileptic and paroxysmal activation methods brain tumours and other space-occupying lesions (with a section on oncological CNS complications) the EEG in cerebral inflammatory processes cerebrovascular disorders and EEG EEG and old age EEG and dementia EEG and neurodegenerative disorders the EEG in infantile brain damage and cerebral palsy craniocerebral trauma metabolic central nervous system disorders cerebral anoxia - experimental view cerebral anoxia - clinical aspects coma and brain death epileptic seizure disorders non-epileptic attacks polygraphy polysomnography - principles and applications in sleep and arousal disorders neonatal electroencephalography event-related potentials - methodology and quantification contingent negative variation and Bereitschafts-potential visual evoked potentials auditory evoked potentials evoked potentials in infancy and childhood neurometric use of event-related potentials event-related potentials - P 300 and psychological implications neuroanaesthesia and intraoperative neurological monitoring clinical use of magnetoencephalography brain mapping - methodology the clinical use of brain mapping EEG analysis - theory and practice the EEG in patients with migraine and other headaches psychiatric disorders and the EEG psychology, physiology and the EEG EEG in aviation, space exploration and diving EEG and neuropharmacology - experimental approach EEG, drug effect and central nervous system poisoning toxic encephalography the special form of stereo-electroencephalography electroencephalography subdural EEG recordings special techniques of recording and transmission prolonged EEG monitoring in the diagnosis of seizure disorders EEG monitoring during carotid endarterectomy and open heart surgery computer analysis and cerebral maturation special use of EEG computer analysis in clinical neurology.

3,211 citations


"Multichannel EEG Compression: Wavel..." refers methods in this paper

  • ...The s/s/t and t/dt/s volume approach achieve the highest CR for the EEG-MMI and BCI3-MI dataset respectively, both for lossless compression (δ = 0) and near-lossless compression with different tolerance values....

    [...]

  • ...The image-based methods are capable of exploiting those short-term correlations effectively, and as a result, they perform as well as the volumetric methods (and even slightly better for the BCI4-MI dataset)....

    [...]

  • ...The compression ratio for a given step size δ is largest for the EEG dataset (EEG-MMI) with lowest sample frequency fs and lowest amplitude resolution, compared to the datasets with higher fs and resolution (BCI3-MI & BCI4-MI)....

    [...]

  • ...The recordings were made in healthy subjects performing motor imagery tasks by the BCI2000 system [26, 27]....

    [...]

  • ...Interestingly, for the BCI4-MI dataset, the image-based scheme performs slightly better in terms of CR compared to volumetric schemes....

    [...]

Book
01 Apr 1993
TL;DR: The main thrust of Electroencephalography is to preserve the sound basis of classic EEG recording and reading and, on the other hand, to present the newest developments for future EEG/neurophysiology research, especially in view of the highest brain functions as mentioned in this paper.
Abstract: The main thrust of Electroencephalography is to preserve the sound basis of classic EEG recording and reading and, on the other hand, to present the newest developments for future EEG/neurophysiology research, especially in view of the highest brain functions. The Fourth Edition features new chapters on modern and future oriented EEG/EP research, spinal monitoring and dipole modelling

3,195 citations

Journal ArticleDOI
TL;DR: This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system.
Abstract: Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.

2,560 citations


"Multichannel EEG Compression: Wavel..." refers methods in this paper

  • ...The recordings were made in healthy subjects performing motor imagery tasks by the BCI2000 system [26, 27]....

    [...]

Journal ArticleDOI
TL;DR: It is proposed that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter-subject variability.

865 citations


"Multichannel EEG Compression: Wavel..." refers methods in this paper

  • ...Motor imagery dataset-I (BCI4-MI): This database consists of 64-channel EEG signals recorded with densely distribute d electrodes in sensorimotor areas [29]....

    [...]