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

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

doi:10.1109/titb.2012.2230012

Cited by 56 publications

(45 citation statements)

References 22 publications

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“…It is generally accepted that there are alterations of EEG rhythms in the AD brain: (1) EEG slowing; that is, a shift of the power spectrum toward the lower frequency bands (delta and theta band) along with a decrease of oscillations in the higher frequency bands (alpha and beta); (2) decreased complexity, which is strongly related to the slowing of EEG; and (3) decreased synchrony, from which the functional network can be extracted (Dauwels et al 2010a(Dauwels et al , b, 2011(Dauwels et al , 2013Stam et al 2003Stam et al , 2005Stam et al , 2007. Notably, AD has been described as a disconnection syndrome, where functional interactions in the brain are strongly affected by anatomical abnormalities among different cortical areas (Jelles et al 1999) and altered cholinergic coupling interactions among cortical neurons (Jeong 2004).…”

Section: Discussionmentioning

confidence: 99%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

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In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pairwise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

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Section: Discussionmentioning

confidence: 99%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

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“…Tensor based methods have found applications in many fields including Chemometrics [20], telecommunications [21] and neuroscience [22], [23]. In this section, we propose a tensor based compression method for traffic data.…”

Section: Data Compression Using Tensor Decompositionmentioning

confidence: 99%

“…We use percent root-mean-square distortion (PRD) [23], [27] to compare compression efficiency of proposed methods. PRD is also often referred as Relative error [28], [29].…”

Section: Compression Efficiencymentioning

confidence: 99%

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Data compression techniques for urban traffic data

Asif

Srinivasan

Dauwels

et al. 2013

2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS)

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Abstract-With the development of inexpensive sensors such as GPS probes, Data Driven Intelligent Transport Systems (D 2 ITS) can acquire traffic data with high spatial and temporal resolution. The large amount of collected information can help improve the performance of ITS applications like traffic management and prediction. The huge volume of data, however, puts serious strain on the resources of these systems. Traffic networks exhibit strong spatial and temporal relationships. We propose to exploit these relationships to find low-dimensional representations of large urban networks for data compression. In this paper, we study different techniques for compressing traffic data, obtained from large urban road networks. We use Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) for 2-way network representation and Tensor Decomposition for 3-way network representation. We apply these techniques to find low-dimensional structures of large networks, and use these low-dimensional structures for data compression.

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“…The Percent-root-mean square distortion (PRD) is a common performance measure to compare the reconstruction performance of low-dimensional models [15]. Interestingly, after decompression, we obtain the exact measurements without errors for some links (corresponding to C); therefore, it is crucial to select the links associated with C in a careful manner.…”

Section: A E-curmentioning

confidence: 99%

CUR decomposition for compression and compressed sensing of large-scale traffic data

Mitrović

Asif

Rasheed

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Intelligent Transportation Systems (ITS) often operate on large road networks, and typically collect traffic data with high temporal resolution. Consequently, ITS need to handle massive volumes of data, and methods to represent that data in more compact representations are sorely needed. Subspace methods such as Principal Component Analysis (PCA) can create accurate low-dimensional models. However, such models are not readily interpretable, as the principal components usually involve a large number of links in the traffic network. In contrast, the CUR matrix decomposition leads to low-dimensional models where the components correspond to individual links in the network; the resulting models can be easily interpreted, and can also be used for compressed sensing of the traffic network. In this paper, the CUR matrix decomposition is applied for two purposes: (1) compression of traffic data; (2) compressed sensing of traffic data. In the former, only data from a "random" subset of links and time instances is stored. In the latter, data for the entire traffic network is inferred from measurements at a "random" subset of links. Numerical results for a large traffic network in Singapore demonstrate the feasibility of the proposed approach.

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

Cited by 56 publications

References 22 publications

Power spectral density and coherence analysis of Alzheimer’s EEG

Power spectral density and coherence analysis of Alzheimer’s EEG

Data compression techniques for urban traffic data

CUR decomposition for compression and compressed sensing of large-scale traffic data

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