Applied Science, Faculty of
Electrical and Computer Engineering, Department of
DSpace
Sensors 16 (2): 201 (2016)
Mahrous, Hesham
Ward, Rabab Kreidieh
This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets.
sensorsArticleBlock Sparse Compressed Sensing ofElectroencephalogram (EEG) Signals by ExploitingLinear and Non-Linear DependenciesHesham Mahrous and Rabab Ward *ECE, University of British Colombia, 2332 Main Mall, Vancouver, BC V6T 1Z4, Canada;hesham.m.mahrous@gmail.com* Correspondence: rababw@ece.ubc.ca; Tel.: +1-604-266-4161Academic Editors: Yun Liu, Han-Chieh Chao, Pony Chu and Wendong XiaoReceived: 6 December 2015; Accepted: 29 January 2016; Published: 5 February 2016Abstract: This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram(EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensorsis limited. For the single EEG channel case, known as the single measurement vector (SMV) problem,the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. Thismethod exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within themeasurement vector of a single channel. For the multichannel case, known as the multi-measurementvector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method hasbeen proposed. This method learns the joint correlation structure in the multichannel signals bywhitening the model in the temporal and the spatial domains. Our proposed method represents themulti-channels signal data as a vector that is constructed in a specific way, so that it has a better blocksparsity structure than the conventional representation obtained by stacking the measurement vectorsof the different channels. To reconstruct the multichannel EEG signals, we modify the parameters ofthe BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependencystructures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsitystructure. The proposed method is shown to significantly outperform existing SMV and also MMVmethods. It also shows significant lower compression errors even at high compression ratios such as10:1 on three different datasets.Keywords: EEG signals; tele-monitoring; compressed sensing; BSBL; multivariate compression;linear and nonlinear dependency1. IntroductionFor tele-monitoring and other applications involving EEG signals in Wireless Body Area Networks(WBANs) the amount of data acquired by the sensor is fairly large. Since the battery life of sensors inWBANs is limited, Compressed Sensing (CS) has been drawing much attention for such applications.CS requires less energy in compressing signals than existing techniques such as transform-basedcompression methods [1–6]. These CS methods have been applied to reconstruct single EEGchannels [1–4,6]. The work in [2,3] proposes a CS framework for EEG signals. This frameworkis compared with those using the following dictionaries: a Gabor dictionary, a Mexican hat (secondderivative of Gaussian function), a linear spline, a cubic spline, and a linear B spline and cubicB-spline. In [7], ECG and EEG signals were reconstructed using a linear B-spline wavelet dictionaryand cubic B-spline matrix and reconstructed using Bayesian Compressive Sensing. The dictionaryis randomly sampled and a modified banded Toeplitz dictionary matrix is formed. Another recentapproach is to apply independent component analysis to pre-processes the EEG signals, prior toSensors 2016, 16, 201; doi:10.3390/s16020201 www.mdpi.com/journal/sensorsSensors 2016, 16, 201 2 of 16applying compressed sensing, so as to improve their sparse representation [8]. Zhang et al. haverecently proposed reconstructed the EEG signals using a Block Sparse Bayesian Learning-BoundedOptimization (BSBL-BO) framework [1]. BSBL-BO reconstructs EEG signals without using a sparsifyingdictionary matrix such as a Gabor dictionary. It is empirically shown to be highly effective inreconstructing EEG signals, as long as a low reconstruction error is tolerated. In [6], a compressivesensing framework is proposed where inter-channel redundancy removal is applied at the sensorafter the sensing stage. Comparing the compression results of this approach with JPEG2000 andBSBL-BO shows that JPEG 2000 achieves the lowest error at high compression rates. However, thepower consumption is of JPEG 2000 is too high and it is thus not suitable for WBAN applications.The above studies have addressed the Single Measurement Vector (SMV) CS case i.e., where singleEEG channels are compressed and decompressed channel by channel. However, the simultaneousreconstruction of the CS signals from their multi-channel measurements (referred to as the MMVproblem) has been shown to recover the signals more accurately than by applying SMV solutions oneach channel separately [9–12]. In [11] the MFOCUSS algorithm extended the diversity minimizationFOCUSS algorithm from an SMV to an MMV algorithm. MFOCUSS is an iterative re-weightedregularized algorithm that solves a l p norm minimization, where 0 < p ď 1 for MMV. In [10], theMFOCUSS was modified to tMFOCUSS by replacing the regularization with the Mahalanobis distanceregularization, to capture the temporal correlation of the MMV signals. A similar idea is used in [9]to capture the temporal correlation, but in this case a Sparse Bayesian framework for MMV signalsis employed. In [12] a STSBL-EM algorithm is proposed to solve the MMV model by exploiting thetemporal and spatial correlation of the signals. It was to achieve somehow lower reconstruction errorscompared to applying BSBL-BO on each channel. In this paper we propose a method to solve the MMVproblem (and the SMV problem). We show that our proposed method outperforms existing algorithmsin terms of reconstruction accuracy at different compression rates using three different datasets.The previously mentioned work has focused only on reducing the power consumption of the datatransmission part of the sensor, and only a few works have addressed the sensing and the processingpower of the sensor node, however, the power consumption is still too high to consider in WBANapplications. Recent studies in [13–15] attempt to reduce the sensing and processing power. In [14] itis argued that to reduce the sensing energy, a smaller number of samples should be acquired directlyin a random fashion. That is the signal should not be fully sampled and then compressed. StandardCS recovery techniques result in high NMSE so an alternative recovery approach based on the theoryof low-rank completion is proposed. Their proposed algorithm is shown to be able to achieve powersavings at a compression ratio of 2:1, the signal recovery is however poor at high compression ratios.In [15] it is shown that by applying blind compressed sensing; (a compressed sensing technique thatinvolves dictionary learning) while solving the synthesis prior formulation, achieves better results. Thework presented in [13] proposes a technique that combines blind compressed sensing with low-rankrecovery (i.e., combining the techniques in [14,15]). This technique achieved on average 0.18 NMSE fora compression ratio of 5:1, using the BCI data set in [16]. All of these techniques are not suitable forWBANs because the recovery quality is not high, hence high NMSE at high compression rates.There are several studies that explain the physiological basis of EEG channels [17]. The EEGsignals contain many redundancies that result in strong inter- and intradependencies within andamong channels [17–20]. In this study, we estimate the linear dependency structure of EEG signals(by using the correlation measure) and the non-linear dependency (by using the phase locking values).We show the existence of linear and non-linear dependencies within and amongst the EEG channels.Unlike BSBL-BO [1] that exploits only the intra-block correlation structure (i.e., linear dependence) todecompress the EEG signals, we modify the BSBL-BO so that it can exploit the linear and non-lineardependencies in EEG signals. The modified algorithm is shown to give better results than BSBL-BO andother existing algorithms, for the single channel (SMV) and the multi-channel (MMV) EEG problems.The modified algorithm reconstructs the multi-channels (MMV problem) by a specific vectorizing ofthe measurement vectors of the channels. We show that the DCT coefficients of this resulting vectorSensors 2016, 16, 201 3 of 16(of the multi EEG channel signals) form a redundant block-sparse structure and that this structurehas linear and nonlinear dependencies. This structure promotes low error even at high compressionrates. We show that our proposed method (called BSBL-LNLD) outperforms many existing MMVmethods and achieves a very low mean square error even at a high compression ratio of 10:1(90% compression rate).2. Background Literature2.1. Compressed Sensing of L Dimensional SignalsAssume the number of EEG channels is L. For the lth channel, the corresponding CS model,denoted as the single measurement vectors (SMV) model is expressed as:yl “ Axl ` vl (1)In Equation (1), the vector xl P RN is the raw signal of the lth channel, yl P RM is the CScompressed signal, and A P RMXN is the measurement matrix (also called the sensing matrix). vl P RNis the channel noise. N is the number of samples of xl , and M is the number of samples after thereduction/compression of xl . Traditional CS recovery algorithms use the compressed data yl , andthe sensing matrix A to recover the original signal xl , the recovered signal is referred to as xˆl . Thesuccess of recovery relies on the key assumption that xl is sparse or has a sparse representation in acertain transform domain. When xl has a sparse representation in a certain domain, then xl can beexpressed as xl “ Dzl , where zl is the sparse representation of xl and D P RNXN is a basis matrix thatsparsifies the signal. For example D can be the discrete cosine transform (DCT) or the discrete wavelettransform (DWT) matrix.Based on the sparsity requirements, to achieve the optimal reconstruction, Equation (1) isre-written as:yl “ ADzl ` vl (2)Given A and D, a Compressed Sensing (CS) rule known as the Restricted Isometric Property(RIP) must be satisfied so that a perfect reconstruction is achieved at a minimum sampling rate givenby [21–24]:M ě µ2 pA, Dq .S.log pNq (3)Equation (3) shows the minimum value of M that can be chosen, so as perfect reconstruction isachieved. In Equation (3), S is the number of non-zero elements in zl , and µ2 is a coherence functionbetween the two matrices A and D. The minimum value that can be chosen for M is dictated by Sand µ2. To achieve maximum incoherence, both matrices A and D should be selected carefully, sothat D achieves a minimum S in sparsifying xl . Much work has been done by previous researchers, tofind the optimal D and A to achieve a minimum M. Unfortunately, finding the optimal D and A tocompress any data or signal is not easy to achieve. Based on Equation (2), one may use a traditional CSalgorithms to estimate zl , and then calculate xˆl , that is:min||zˆl||l1 subject to ||ADzˆl ´ yl||l2 (4)xˆl “ Dzˆl (5)Equation (4) uses the Euclidian distance and minimizes the l1 norm of zˆl . However, for highlycorrelated signals in the multivariate domain such as in EEG signals, the work presented in [9]shows that minimizing the Mahalanobis distance signal measurement achieves better results forhighly correlated signals in the multivariate domain such as in EEG signals. The result of using theMahalanobis distance is presented in Section 4.Sensors 2016, 16, 201 4 of 162.2. Block Sparse Bayesian Learning via Bounded Optimization (BSBL-BO)BSBL-BO is a CS framework [24] that has been recently proposed for solving theSingle-Measurement-Vector (SMV) model (i.e., the model Equation (1) with L = 1). While someCS algorithms depend on the sparsity of the signal, BSBL-BO exploits the block sparsity in the signal,provided the signal is block sparse [1]. That is, BSBL-BO assumes that the vector xl consists of(g non-overlapping) blocks and some of these blocks are all zeros. As mentioned in [1,24], the blocksize can be arbitrarily and the block partition does not need to be consistent with the true blockstructures of the signal.Raw EEG signals generally do not have clear block structures in the time domain. Therefore,BSBL-BO is applied on the DCT coefficients of the signals [1]. By using the DCT dictionary matrix, anEEG signal is expressed as a DCT coefficient vector, where the coefficients with significant nonzerovalues concentrate at the lower frequencies of the coefficient vector (from the “energy compaction”property of DCT). The coefficient vector can be viewed as a concatenation of one or more nonzeroblocks followed by a number of zero blocks. Therefore, BSBL-BO can exploit this specific block sparsityin the DCT coefficients by first obtaining the DCT coefficients and then reconstructing the originalEEG signal.The BSBL-BO algorithim is derived by applying type II maximum likelihood derivation [24] ofthe posterior probability given as:p´xˆˇˇˇy, λ, tγi, Biugi“1¯“ N pµx,Σxq (6)The hyperparameters λ, tγi, Biugi“1 represent the noise (λ), the block sparsity structure (γi), andthe intra-correlation structure (Bi) in the non-overlapping blocks g. Let Σ0 be a diagonal matrixsuch that Σ0 “ diag´tγiBiugi“1¯. After estimating the hyper-paramteres, the reconstructed signal xˆis estimated by minimizing the negative log-likelihood Equation (6). The resulting estimate of thereconstructed signal is given as: xˆ “ µx “ Σ0AT`λI`AΣ0AT˘´1 y.The hyperparameters λ andγi are derived based on the bound-optimization estimation in [24].λ is a scalar that helps the algorithim perform in noisy conditions. In noiseless cases, λ is fixedto a small value, e.g., λ “ 10e´10 but when the SNR is less than 15 dB, λ is estimated using thebounded optimization technique given in [24]. This yields λÐ||py´Aµxq||22 `řgi“1 Tr´Σix`Ai˘T Ai¯M.The hyperparameter γi is a nonnegative scalar that controls the block-sparsity of the signal.When γi = 0, the corresponding xˆ of the ith block = 0. This hyperparaemter is given asγi Ðgffe xTi B´1i xiTr´pAiqTpΣy˚q´1AiBi¯ .The other hyperparameter, Bi P Rdiˆdi is a positive definite matrix that captures theintracorrelation structure of the ith block. di is the number of samples of the ith block. Theintra-correlation is useful because it indicates a predictive relationship that can be exploited.Equation (7) below is a covariance matrix which is derived by minimizing the negative log-likelihoodof the posterior probaility Equation (6). Bi is further modified to obtain Bˆi Equation (8) by constrainingit to be a positive definite intracorrealtion matrix. Bˆi is formed using a first-order Auto-Regressive(AR) process which is sufficient to model the intra-block correlation [24]. The resulting Bˆi is a Toeplitzmatrix that is selected to represent the intra-block correlation matrx Bi:Bi “ 1ggÿi“1Σix ` µix`µix˘Tγi(7)Sensors 2016, 16, 201 5 of 16Bˆi “ Toeplitz´”1, r, . . . rdi´1ı¯“»—– 1 r ¨ ¨ ¨ rdi´1.... . ....rdi´1 rdi´2 ¨ ¨ ¨ 1fiffifl@i (8)The first order Auto-Regressive coefficient is r “ m1m0, where m0 & m1 are the average of theelements of the main diagonal and sub-diagonals of the estimated covariance matrix Bi. In BSBL-BO,Bˆi captures the intra-block correlation structure by converting the estimated covariance matrix Bi to abounded first order Toeplitz matrix. The intra-block correlation is a measure of linear dependency. Inthe next section, we modify the BSBL-BO so it can exploit both the linear dependency structure as wellas the non-linear dependency structure in EEG signals.3. Approach and Implementation3.1. ApproachIt has been shown that better reconstruction can be obtained by exploiting the block-sparsity(assuming the data vector is block sparse) than by only exploiting the sparsity in the signal (assumingthe vector is sparse in the conventional sense) [1,25,26]. The conventional sparsity solution methodonly assumes that xl has at most S non-zero elements, in a sparse domain. However, it does not exploitany further structure that the signal may have. The non-zero components can appear anywhere inxl , however, there are cases in which the non-zero values can form blocks [26]. We propose to applythe block-sparse recovery approach to the EEG multiple measurement vector (MMV) problem as theMMV data share a joint sparsity pattern [27–29]. For the case of EEG signals, the channels have linearand non-linear dependencies structure between them as well as within a channel [19,20,29].The work presented in [1] addresses the SMV case. It uses a DCT dictionary matrix (that resultsin energy compaction of a single EEG channel) to obtain a vector of block sparse DCT coefficients.These DCT coefficients are recovered by BSBL-BO in [1]. To study the MMV case, we first investigatethe structure of the MMV data vector. For the MMV case let X be the matrix rx1, x2, . . . xL s whereL is the number of channels. In conventional studies vec rXs i.e., the vector formed by stacking thecolumns of X has been studied. However in this paper we propose to study vecrXTs i.e., the vectorformed by stacking the rows of X as a vector. The DCT coefficients of vecrXTs and vec rXs are shownin Figure 1a,b, respectively. These correspond to the case when the number channels L is 23. The DCTcoefficients of Figure 1c are the DCT transform of xl , when xl is formed of 23 s of data of the channel l,and the DCT coeficients of Figure 1d are the DCT transform of xl when it is formed of one second ofdata of the same channel. In Figure 1c, the length of the channel was formed of 23 s so as to result inthe same number of coefficents as those of Figure 1a,b. This paper compresses the DCT coefficients ofmulti-channel EEG signals, which we denote as DTvecrXTs. DTvecrXTs has more of a block sparsestructure than the vector formed by concatenating the channels of the EEG signals, i.e., DTvecrXs. Theblocks in DTvecrXTs also have more structure than the DCT coefficients of a single channel whichwe denote as DTxl . Figure 1a shows that the MMV vector, DTvecrXTs, exhibits more structure andmore redundant non-zero blocks than the vector formed by concatenating the channels, DTvecrXs,(Figure 1b) and DTxl (Figure 1c,d). This is investigated further in more details in Sections 3.2.3 and 4.3.BSBL-BO exploits the intra-block correlation, which is a measure of linear dependency in theblocks of a single channel data (temporal data only). Previous works however show that EEGsignals and neurophysiological signals exhibit linear as well as non-linear dependencies [19,20].In [19], EEG signals are examined for non-linear interdependence between channels, and a significantevidence of the existence of non-linear interdependency is shown. In order to describe the structureof EEG signals, the work in [20] suggests that nonlinear dependency measures, such as: mutualinformation, entropy, phase synchronization, and state space synchronization are not intended tosubstitute linear dependency measures such as auto regression (AR) and cross correlation. Instead,Sensors 2016, 16, 201 6 of 16non-linear dependency must be regarded as a complement to the linear dependency when studyingEEG signals. This allows a more comprehensive structure in the EEG data.Based on our observation above, we therefore vectorize the signals of the multichannel data asvecrXTs i.e., in a way that is different from the conventional one. This will help us better exploit theblock-sparse structure of vecrXTs exhibited in Figure 1a. We also show that this resultant multichannelvector has significant linear and non-linear dependencies. In our method, the compressed data isreconstructed using this vectorization in conjunction with a modified version of BSBL-BO, which willbe presented in Section 3.2.4. As will be shown, we will modify the matrix Bˆi Equation (8) in BSBL_BO.This matrix is Toeplitz and its AR coefficients model the intra-channel correlation for every channel byexploiting the intra-block correlation of each EEG channels. The modified version of Bˆi combines thePhase Locking Values (PLV) of the blocks (so as to exploit the non-linearity intra-dependence) with theintra-channel correlation. The use of DvecrXTs instead of processing single channels, would enablethe exploitation of the intra-blocks interdependencies and modelling the intra and inter dependencies(whether linear dependence, non-linear dependence, or both) of channels. Applying the modifiedBSBL-BO on the vector from the suggested vectorization (vecrXTswill enable us to exploit the linearand non-linear dependencies within the channels of the EEG data as well as between the channels.The detail of the modified algorithm follows in the next subsection.Sensors 2016, 16, 201 6 of 15 correlatio for every hannel by exploiting the intra-block correlation of each EEG channels. The modified version of ܤప෡ c mbines the Phase Locking Values (PLV) of the blocks (so as to exploit the non-linearity intra-dependence) with the intra-channel correlation. The use of ܦݒ݁ܿ[்ܺ] instead of processing single channels, would enable the exploitation of the intra-blocks interdependencies and modelling the intra and inter dependencies (whether linear dependence, non-linear dependence, or both) of channels. Applying the modified BSBL-BO on the vector from the suggested vectorization (ݒ݁ܿ[்ܺ] will enable us to exploit the linear and non-linear dependencies within the channels of the EEG data as well as between the channels. The detail of the modified algorithm follows in the next subsection. Figure 1. Block Sparsity of EEG DCT Coefficients of EEG channels. (a) The DCT coefficients of ݒ݁ܿ[்ܺ]; (b) The DCT coefficients of ݒ݁ܿ[ܺ]; (c) The DCT coefficients of ݔ௟, ݓℎ݁݊ ݔ௟ is formed of 23 s of data of the channel l; (d) The DCT coefficients of ݔ௟ when it is formed of one second of data of the same channel. 3.2. Implementation The corresponding CS model for the L channels case, denoted as the MMV problem is expressed as ܻ = ܣܺ + ܸ , where ܻ = [ݕଵ, ݕଶ, … ݕ௟], ܺ = [ݔଵ, ݔଶ, … ݔ௟] , ܸ = [ݒଵ, ݒଶ, … ݒ௟] , (X ∈ ܴே௑௅, Y ∈ ܴெ௑௅ , V ∈ ܴே௑௅), and N is the number of samples per epoch. In this paper, the matrix ்ܺ is vectorized so that the measurement vector is represented as ݕ = ܣݒ݁ܿ[்ܺ] + ݒ . The implementation of the compression technique is shown in Figure 2 and implementation is discussed in the following subsections. 3.2.1. Epoching The EEG data of each channel is divided into non-overlapping epochs each of size N. In our experiments, we choose N to be equal to the sampling frequency of the dataset i.e., it corresponds to one second. After the compression, the data of each epoch are recovered independently from other epochs. 3.2.2. Channel Arrangement and Vectorization In [1], BSBL-BO was developed to decompress a single vector and was thus applied on each SMV channel. To compress the multiple channels, in this paper the BSBL-BO in [1] is modified and then applied to reconstruct the channels jointly and exploits the linear and nonlinear dependencies of the EEG signals. Given a data matrix X ∈ R୒ଡ଼୐, whose columns are the data of the L channels then (as mentioned above) the matrix X is transformed into the vector ݒ݁ܿ[்ܺ] =ൣݔଵ,ଵ, ݔଶ,ଵ, … ݔ௅,ଵ, ݔଵ,ଶ,ݔଶ,ଶ … ݔ௅,ଶ … ݔଵ,ே … ݔ௅,ே൧் , and also into the vector ݒ݁ܿ[ܺ] =Figure 1. Block Sparsity of EEG DCT Coefficients of EEG channels. (a) The DCT coefficients of vecrXTs;(b) The DCT coefficients of vecrXs ; (c) The DCT coefficients of xl , when xl is formed of 23 s of data ofthe channel l; (d) The DCT coefficients of xl when it is formed of one second of data of the same channel.3.2. ImplementationThe corr sponding CS model for the L channels case, denot t e MV problem is expr ssed asY “ AX` V, where Y “ ry1, y2, . . . yls , X “ rx1, x2, . . . xls, V “ rv1, v2, . . . vls, (X P RNXL, Y P RMXL,V P RNXL), and N is the number of samples per epoch. In this paper, the matrix XT is vectorized so thatthe measurement vector is represented as y “ AvecrXTs ` v. The implementation of the compressiontechnique is shown in Figure 2 and implementation is discussed in the following subsections.3.2.1. EpochingThe EEG data of each c annel is divided int -overla ping epochs each of size N. In ourexperiments, we choose N to be equal to the sampling frequency of the data , it o responds to onesecond. After the compression, the data of each epoc are recovered independe tly from other epochs.3.2.2. Channel Arrangement and VectorizationIn [1], BSBL-BO was developed to decompress a single vector and was thus applied oneach SMV channel. To compress the multiple channels, in this paper the BSBL-BO in [1] isSensors 2016, 16, 201 7 of 16modified and then applied to reconstruct the channels jointly and exploits the linear and nonlineardependencies of the EEG signals. Given a data matrix X P RNXL, whose columns are thedata of the L channels then (as mentioned above) the matrix X is transformed into the vectorvecrXTs “ rx1,1, x2,1, . . . xL,1, x1,2,x2,2 . . . xL,2 . . . x1,N . . . xL,NsT , and also into the vector vec rXs “rx1,1, x2,1, . . . xN,1, x1,2,x2,2 . . . xN,2 . . . x1,L . . . xN,LsT . When vecrXTs is divided into non-overlappingblocks, d1is, such that di ą 2L then each block of vecrXˆTs would contain both temporal and spatialinformation about the data. It is thus important that di ą 2L otherwise temporal correlation wouldbe neglected.Sensors 2016, 16, 201 7 of 15 ൣݔଵ,ଵ, ݔଶ,ଵ, … ݔே,ଵ, ݔଵ,ଶ,ݔଶ,ଶ … ݔே,ଶ … ݔଵ,௅ … ݔே,௅൧். When ݒ݁ܿ[்ܺ] is divided into non-overlapping blocks, ݀௜′ݏ , such that ݀௜ > 2ܮ then each block of ݒ݁ܿ[ ෠்ܺ] would contain both temporal and spatial information about the data. It is thus important that ݀௜ > 2ܮ otherwise temporal correlation would be neglected. Figure 2. Block Diagram showing our approach for multivariate compression in CS. As shown in Figure 1a the DCT coefficients of ݒ݁ܿ[்ܺ] exhibit better block sparsity than the DCT coefficents of vec[ܺ] and of ݔ௟ shown in Figure 1b–d. The structure shown in Figure 1a is found to be consistent for different data samples. The DCT of ݒ݁ܿ[்ܺ] shows that this distinct block-sparse structure has a redundant form. In Figure 1a the non-zero values forms blocks that repeat in a consistant fashion. This structure does not exist for uncorrelated signals. To prove this emperically, the DCT of ݒ݁ܿ[்ܺ] is examined when ்ܺ݅ݏ ݂݋ݎ݉݁݀ ݋݂ uncorrelated and of correlated random variables as show in in Figure 3a–d Figure 3a shows the DCT coefficients of the vectorized form of uncorrelated random signals. Figure 3b–d shows the DCT coefficients of the vectorized forms of correlated multichannel signals generated of random multi-channels variables. As shown in Figure 3b–d, the DCT coefficients of the vectorized correlated signals exhibit a distinct block sparse-structure. The redundancy of the non-zero structure increases by increasing the number of channels. As the number of correlated channels increases the number of structured non-zero blocks increases and this increases the accuracy of the recovery for high compression rates. This is illustrated in Figure 3b–d. Figure 3. Block structure of correlated and uncorrelated signals in the DCT domain. (a) The DCT coefficients of the vectorized form of uncorrelated random signals; (b) The DCT coefficients of the vectorized forms of correlated 6 channel signals; (c) The DCT coefficients of the vectorized forms of correlated 10 channel signals; (d) The DCT coefficients of the vectorized forms of correlated 14 channel signals. 3.2.3. Compression As mentioned in Section 2.1, the compression effectiveness depends on the degree of coherence between the matrices ܣ and ܦ. It is shown in [5,21,22] that the higher the incoherence between ܣ Figure 2. Block Diagram showin roach for multiv riate compress on in CS.As shown in Figure 1a the DCT coefficients of vecrXTs exhibit better block sparsity than the DCTcoefficents of vec rXs and of xl shown in Figure 1b–d. The structure shown in Figure 1a is found tobe consistent for different data samples. The DCT of vecrXTs shows that this distinct block-sparsestructure h s a redundant form. In Figure 1a the non-zero values forms blocks that repeat in a onsistantfashion. This structure does not exist for uncorrelated signals. To prove this emperically, the DCT ofvecrXTs is examined when XTis f ormed o f uncorrelated and of correlated random variables as showin in Figure 3a–d Figure 3a shows the DCT coefficients of the vectorized form of uncorrelated randomsignals. Figure 3b–d shows the DCT coefficients of the vectorized forms of correlated multichannelsignals generated of random multi-channels variables.As shown in Figure 3b–d, the DCT coefficients of the vectorized correlated signals exhibit adistinct block sparse-structure. The redundancy of the non-zero structure increases by increasingthe number of channels. As the number of correlated channels increases the number of structurednon-zero blocks increases and this increases the accuracy of the recovery for high compression rates.This is illustrated in Figure 3b–d.Sensors 2016, 16, 201 7 of 15 ൣݔଵ,ଵ, ݔଶ,ଵ, … ݔே,ଵ, ݔଵ,ଶ,ݔଶ,ଶ … ݔே,ଶ … ݔଵ,௅ … ݔே,௅൧். When ݒ݁ܿ[்ܺ] is divided into n n-overlapping bl cks, ݀௜′ݏ , such that ݀௜ > 2ܮ then each block of ݒ݁ܿ[ ෠்ܺ] would contain both temporal and spatial information about the data. It is thus important that ݀௜ > 2ܮ otherwise temporal correlation would be neglected. Figure 2. Block Diagram showing our approach for multivariate compression in CS. As hown in Figu 1a the DCT coefficients of ݒ݁ܿ[்ܺ] ex ibit better block sparsity than the DCT co fficents of vec[ܺ] and of ݔ௟ shown in Figure 1b–d. The structure shown in Figure 1a is found t be co sistent for different data samples. The DCT of ݒ݁ܿ[்ܺ] shows that this dis inct block-sparse structure has a redundant form. In Figure 1a the non-zero values forms blocks th repeat in a consistant fashion. This structure oes not exi t for uncorrelated signals. To pro this emperically, the DCT of ݒ݁ܿ[்ܺ] is examined when ்ܺ݅ݏ ݂݋ݎ݉݁݀ ݋݂ uncorrelated and of correlated random va iabl s as show i in Fi ure 3a–d Figure 3a sh ws the DCT co fficients of the vectorized form of uncorrelated ra dom signals. Figure 3b–d shows the DCT coeffici nts f the vectorized forms of correlated multichannel signals g n rate of random multi-channels variables. As show in Figure 3b–d, the DCT coefficients of the vectorized correlated signals exhibit a distinct block sp rse-structure. The r dund ncy of the non-zero structure increases by inc easing the number of channels. As the number of correlated channels increases the number of structured non-zero blocks increases and this increases the accuracy of the recovery for high compression rates. This is i rated in Figure 3b–d. Figure 3. Block structure of correlated and uncorrelated signals in the DCT domain. (a) The DCT coefficients of the vectorized form of uncorrelated random signals; (b) The DCT coefficients of the vectorized forms of correlated 6 channel signals; (c) The DCT coefficients of the vectorized forms of correlated 10 channel signals; (d) The DCT coefficients of the vectorized forms of correlated 14 channel signals. 3.2.3. Compression As mentioned in Section 2.1, the compression effectiveness depends on the degree of coherence between the matrices ܣ and ܦ. It is shown in [5,21,22] that the higher the incoherence between ܣ Figur 3. Block struct re of correlated and unco l t signals in the DCT domain. (a) The DCTcoeffi i nts of the vectorized form of uncor elat o signals; (b) The DCT coefficients of thevectorized forms of correlated 6 channel i als; (c) The DCT coefficients of the vectorized formsof correlated 10 channel signals; (d) The DCT coefficients of the vectorized forms of correlated14 channel signals.Sensors 2016, 16, 201 8 of 163.2.3. CompressionAs mentioned in Section 2.1, the compression effectiveness depends on the degree of coherencebetween the matrices A and D. It is shown in [5,21,22] that the higher the incoherence between Aand D, the larger the compression that can be achieved. This is valid only when the R.I.P conditionapplies. Regardless of the type of D, to achieve maximum incoherence, A should be independentand identically Gaussian distributed [5]. Using a Gaussian Nˆ0,1N˙results in an optimal sensingmatrix [5], but the generation of such a matrix is computationally expensive. For energy savingpurposes, a sparse binary matrix that contains few non-zeros (of value equal to one) in each columnof the A matrix was used in [1,6]. It was shown that two non-zero entries are sufficient to compressthe signals when the positions of the non-zeros entries are randomly selected in each column of A [1].Also, it was shown that the sparse binary matrix performs as well as using a Gaussian matrix [6].For the MMV problem, to compress vecrXTs for every epoch, we use A P RLM X LN , where Mis the number of random projections that determines the compression ratio given by N{M (or thecompression rate percentage CR% “ˆ1´ MN˙100). L is the number of channels of the EEG signals.To solve the MMV problem, the compressed data is given by y “ A vecrXTs, where y P RLM. Incase of the SMV problem, yl “ Axl , where yl P RM and A P RM X N . The matrix A is fixed for themeasurement of all epoches.3.2.4. Modification of BSBL-BO (BSBL-LNLD)As mentioned in Section 2.2, BSBL-BO exploits the block-sparse structure by learning thehyper-parameter γi. It exploits the intra-block correlation (in a single channel) by learning thehyper-parameter Bˆi. The hyper-parameter Bi is evaluated by minimizing the negative log-likelihoodwith respect to Bi [24]. The resultant derivative is shown in Equation (7). Bi, is transformed to Bˆi byconstraining it to being a positive definite and symmetric matrix. Assuming all the blocks have thesame size, the idea is to find one parameter from which a close estimate of Bi is formed. The Bˆi formedusing the parameter, r, is especially close to Bi along the main diagonal and the main sub-diagonalof Bi [24]. Further, it is found that for many modelling applications, if the elements of a block form afirst-order Auto-Regressive (AR) process then this is sufficient to model the intra-block correlation [30].In this case, the covariance matrix Bi of the block (Equation (6)) is converted to a Toeplitz matrix Bˆi asshown in Equation (8). The parameter r is the AR coefficient. Instead of estimating r from the BSBLcost function, it is empirically calculated as r “ m1m0. For most of the time m0 is greater than m1, whichmakes r ă 1. If for any case r ą 1, then r is constrained to be equal 0.99. This is done in order toinsure that Bi is always invertible.To exploit the linear and nonlinear dependencies, we modify r so that it can also learn thenon-linear dependency and not only the linear one, but the rest of the algorithm remains unchanged.The Phase Locking Value (PLV) between each block of DvecrXˆTs and every other block is calculated.Here Xˆ is a matrix of size N x L that represents the reconstructed signal at every learning iteration. ThePLV between every two non-overlapping blocks is calculated and then averaged to become a scalar p.This scalar value, p, represents the average phase synchronization between all blocks. Since each blockof vecrXˆTs contains temporal and spatial information about the EEG signals, then p captures the intraand inter non-linear dependence in the EEG channels.The PLV was proposed in [31] to measure the phase relationship between two neuro-electric orbio-magnetic signals. The PLV between two signals varies from 0 to 1. When the PLV between twosignals is equal to one, this implies that the signals are perfectly synchronized. When the PLV betweentwo signals is equal to zero this implies that the signals are completely out of synchronization. ThePLV between two signals is given as:Sensors 2016, 16, 201 9 of 16PLV “ 1NNÿi“1exp`j`∅1,i ´∅2,i˘˘(9)where ∅1,i ´∅2,i will now be explained: to obtain Equation (9), each signal is converted into theHilbert space to obtain a real and imaginary part for each sample of the signal i. The phase angle ∅1,iand ∅2,i are then obtained by calculating arctan of the real and imaginary values. Thus ∅1,i ´∅2,iis the phase angle difference between two signals in each sample. Phase synchronization is usefulbecause the phase component is obtained separately from the amplitude component. Unlike coherence,it does not separate the effects of amplitude and phase in the interrelations between two signals.To exploit the information about the phase synchronization in BSBL-BO, the parameter r ismodified so that it learns the linear and also the non-linear dependencies in a vector. Thus whenapplied to vector DvecrXˆTs which contains the inter- and intra-information about the multi channels, rwould learn both types of dependencies in the channels. As such r would contain information aboutinter relationships between channels and not only intra relationships in each channel. This would thenallow BSBL-BO to exploit the inter- and intra linear/non-linear dependence in the channels instead ofthe linear dependency (intra-correlation) only. The modified r is given as r “ˆm1m0` p˙{2, where pis the average of the PLV between the blocks. In the experiments section we show the performanceof the modified version, BSBL-LNLD, in a compressed sensing framework for both SMV and MMVproblems (we denote our modified algorithm as BSBL-LNLD).4. Experiments and Results4.1. Data SetThe experiments were conducted on three datasets. Dataset 1 is the BCI Competition IV [16]. Thisdataset is recorded from seven healthy subjects. The recording was made using 59 EEG channels persubject at a sampling rate of 1000 Hz. Dataset 2 is a collection of 108 polysomnographic recordingsof different sleep disorders monitoring. Each recording contains between five and 13 EEG channelssampled at 256 Hz [32]. The third dataset consists of seizure data of 22 pediatric subjects. Therecordings contain 23 EEG channels at a sampling rate of 256 Hz [33].4.2. Dependence Measure of Intra and Inter EEG BlocksIn this experiment, we study the correlation and the PLV measures over the same data samples. Atotal of 3000 samples were used, 1000 samples from each dataset. Each sample of the EEG data wasselected randomly from a dataset and was over a one second window of the multiple channels. Eachsample was thus formed of the data of all channels in that dataset, each sample was transformed intoits DCT coefficients and the vectors, DTvecrXTs, DTvec rXs , & DTxl were formed. Each of these threevectors was divided into equal size blocks, then the intrablock and interblock correlations and PLVwere calculated for each vector. Then the absolute value of these measures were averaged over all1000 samples taken from the dataset it belongs to this experiment was then repeated few times but fordifferent block sizes. That is each sample was again divided into equal size blocks but for a differentblock size. The results of correlation and PLV of different block sizes are shown in Figure 3. Correlationis a measure of linear dependency and it ranges between ´1 and 1. When the correlation measure ispositive, then the average block correlation is a positive correlation. When the correlation measure isnegative, then it is a negative correlation. The phase synchronization measures such as the PLV findsa statistical relationship between two signals and varies from 0 to 1. PLV is important because EEGsignals are noisy and chaotic, thus two or more EEG signals may have their phases synchronized evenif their amplitudes are uncorrelated over time [20].Figure 4 shows the average correlations and PLV of the randomly selected samples. The vectorDTvec`XT˘has the most intra-correlation structure in the blocks (over 1000 repetitions). We usedSensors 2016, 16, 201 10 of 16Matlab version 8.5 on a PC with 12 GB RAM, and a 2.8 GHz Core i7 intel CPU. Our results agreewith [19,20] that there exist non-linear dependency between the EEG channels and also within eachchannel. Unlike the correlation measure, the PLV is invariant to the type of vector (i.e., it gives the sameresults for the DTvecrXTs, DTvec rXs, DTxl). Although the PLV shows a more dominant dependency,it is not meant to replace the correlation type dependency [20]. For this reason we have added the PLVmeasure in Equation (8) of the BSBL-BO algorithm so as to exploit the non-linear dependency in theEEG signals.Sensors 2016, 16, 201 10 of 15 (a) (b) (c) (d) Figure 4. Mean Correlation and PLV in the blocks and between the blocks. (a) Average Correlation in the blocks; (b) Average Correlation between the blocks; (c) Average PLV in the blocks; (d) Average Correlation between the blocks. 4.3. Error Metrics and Experiements The original data are compressed at different compression rates given by ܥܴ% = (1 − ெே)100. Please note that the compression ratio ேெ = 2: 1 corresponds to CR% = 50%. In our experiments we compress the EEG datasets at different rates including, 50, 60, 70, 80, and 90% compression rates. For these expriements, windows of 4 s each were randomly selected from the three datasets, 200 windows from each channel and for each subject. We measured the reconstruction quality using the Normalized Mean Square Error (NMSE). During the recording of the EEG datasets, a voltage DC bias was present in the electrodes. This bias could cause misleading results when calculating NMSE, to avoid this bias the mean of each signal is subtracted. For a fair evaluation we use the NMSE proposed in [6], ܰܯܵܧ(ݔ, ݔො) = ‖௑ି௑෠‖‖௑ିఓ೉‖. 4.4. Compression/Decompresion Results The compression was done using the sparse binary matrix as a sensing matrix. This matrix has only two non-zeros (i.e., ones) in each column selected at random. The length of the matrix depends on ܯ and this depends on the compression rate. For the MMV problem, our proposed BSBL-LNLD, and the BSBL-BO algorithims are applied. Based on Figure 4, the smaller the block size (i.e., the more number of blocks per epoch), the higher the dependency measures. This however, causes a slow performance in Matlab 8.5. For this reason, a block size of 92 is chosen because it is found to be a suitable tradeoff. Figure 5 shows the performance of our proposed MMV method as the number of EEG channels increases. The more EEG channels (spatial data), the lower the reconstruction error. This is because the spatial dependence between channels increases as the number of channels increase. This promotes more joint dependency which makes the decompression more immune to compression rate values. In our experiments we compared the performance of our proposed MMV method (BSBL-LNLD) with the following state of the art decompression EEG algorithms as: Figure 4. Mean Correlation and PLV in the blocks and between the blocks. (a) Average Correlationin the blocks; (b) Average Correlation between the blocks; (c) Average PLV in the blocks; (d) AverageCorrelation between the blocks.4.3. Error Metrics and ExperiementsThe original data are compressed at different compression rates given by CR% “ˆ1´ MN˙100.Please not that the compression ratioNM“ 2 : 1 corresponds to CR% = 50%. In our experiments wecompress the EEG datasets at different rates including, 50, 60, 70, 80, and 90% compression rates. Forthese expriements, windows of 4 s each were randomly selected from the three datasets, 200 windowsfrom each channel and for each subject. We measured the reconstruction quality using the NormalizedMean Square Error (NMSE). During the recording of the EEG datasets, a voltage DC bias was presentin the electrodes. This bias could cause misleading results when calculating NMSE, to avoid thisbias the mean of each signal is subtracted. For a fair evaluation we use the NMSE proposed in [6],NMSE px, xˆq “ ||X´ Xˆ||||X´ µX||.4.4. Compression/Decompresion ResultsThe compression was done using t e spars binary matrix as a sensing matrix. This matrix hasonly two non-z ros (i.e., ones) in each column selected at random. The l ngth of the matrix dependson M and this pends on the compression rate. For the MMV problem, our proposed BSBL-LNLD,and the BSBL-BO algorithims are applied. Based on Figure 4, the smaller the block size (i.e., the morenumber of blocks per epoch), the higher the dependency measures. This however, causes a slowSensors 2016, 16, 201 11 of 16performance in Matlab 8.5. For this reason, a block size of 92 is chosen because it is found to be asuitable tradeoff.Figure 5 shows the performance of our proposed MMV method as the number of EEG channelsincreases. The more EEG channels (spatial data), the lower the reconstruction error. This is because thespatial dependence between channels increases as the number of channels increase. This promotesmore joint dependency which makes the decompression more immune to compression rate values.In our experiments we compared the performance of our proposed MMV method (BSBL-LNLD)with the following state of the art decompression EEG algorithms as:(1) The tMFOCUSS proposed in [10] is a modified version of the MFOCUSS. It works by capturingthe temporal correlation in the channels. The modification lies in replacing the norm minimizationwith the Mahalanobis distance.(2) The TMSBL method proposed in [9]. It is a Bayesian approach. It defines the signal in thehyper-parameter space instead of the Euclidian space such as in l1/l2 minimization techniques.The hyper-parameter space is defined by temporal correlation and sparse modelling, thisapproach is called the Automatic Relevance Determination as proposed in [9,34]. By usingExpected Maximization, the hyper-parameters are estimated from the posterior information,which is derived from a prior and the log-likelihood of the compressed signal. One can arguethat TMSBL is similar to BSBL-BO in its basic approach and derivation. However, BSBL-BOreconstructs the signal in blocks form, unlike TMSBL.Sensors 2016, 16, 201 11 of 15 (1) The tMFOCUSS proposed in [10] is a modified version of the MFOCUSS. It works by capturing the temporal correlation in the channels. The modification lies in replacing the norm minimization with the Mahalanobis distance. (2) The TMSBL method proposed in [9]. It is a Bayesian approach. It defines the signal in the hyper-parameter space instead of the Euclidian space such as in l1/l2 minimization techniques. The hyper-parameter space is defined by temporal correlation and sparse modelling, this approach is called the Automatic Relevance Determination as proposed in [9,34]. By using Expected Maximization, the hyper-parameters are estimated from the posterior information, which is derived from a prior and the log-likelihood of the compressed signal. One can argue that TMSBL is similar to BSBL-BO in its basic approach and derivation. However, BSBL-BO reconstructs the signal in blocks form, unlike TMSBL. Figure 5. NMSE vs Number of Channels of proposed method at different compression % rates. (3) Recently, the BSBL-BO approach [1] was compared with the STSBL-EM algorithm presented in [12]. The comparison was performed on BCI data at different compression rates such as 50%, 60%, 70%, 80%, and 90%. It was shown that the decompression of SMV BSBL was less accurate than STSBL-EM. Two learning hyper-parameters were introduced in STSBL-EM, to capture the correlation between the blocks in the temporal and spatial domains. STSBL-EM learns the parameters by temporally whitening the model at first, and then the spatial hyper-parameter is learned and the signals are estimated. Then the signals are spatially whitened and then the temporal hyper-parameter and the signals are estimated. This process repeats until convergence within 2 to 3 s on average. The repetitive whiting of the model reduces the correlation in the signals which causes less redundancy during decompression, hence less correlation amongst the blocks. Our results in the Table 1 show that compared to the other methods STSBL-EM does not achieve low errors at high compression rates. The DCT transform matrix is used for all experiments for all the algorithms, the proposed BSBL-LNLD, tMFOCUSS, TMSBL, STBSL, and BSBL-BO methods. These five methods were applied on the MMV problem. For the SVM problem only BSBL-LNLD and BSBL-BO, were applied as the others are only applicable to the MMV problem. Thus single channels are compressed channel by channel for the SMV problem, and multiple channels are compressed simultaneously in case of the MMV problem. For the SMV problem, the EEG data is compressed for each vector such that ݕ௟ = ܣݔ௟, ∀݈ =[1,2, . ., ܮ]. In case of the MMV problem, the EEG signals were compressed using the vector ݕ =ܣݒ݁ܿ[்ܺ]. As shown above the proposed method can sustain good recovery results even at high compression rates e.g., CR 90% (10:1 compression ratio). To the best of our knowledge, these are the best results that have been achieved so far with respect to obtaining high compression rates and low construction errors for the EEG signals in compressed sensing. Not ignoring that fact that the JPEG2000 still achieves the most accurate results at high compression rates, however, it is not suitable for WBANs. Figure 5. NMSE vs Number of Channels of proposed method at different compression % rates.(3) Recently, the BSBL-BO approach [1] was compared with the STSBL-EM algorithm presentedin [12]. The comparison was performed on BCI data at different compression rates such as50%, 60%, 70%, 80%, and 90%. It was shown that the decompression of SMV BSBL was lessaccurate than STSBL-EM. Two learning hyper-parameters were introduced in STSBL-EM, tocapture the correlation between the blocks in the temporal and spatial domains. STSBL-EM learnsthe parameters by temporally whitening the model at first, and then the spatial hyper-parameteris learned and the signals are estimated. Then the signals are spatially whitened and then thetemporal hyper-parameter and the signals are estimated. This process repeats until convergencewithin 2 to 3 s on average. The repetitive whiting of the model reduces the correlation in thesignals which causes less redundancy during decompression, hence less correlation amongst theblocks. Our results in the Table 1 show that compared to the other methods STSBL-EM does notachieve low errors at high compression rates.The DCT transform matrix is used for all experiments for all the algorithms, the proposedBSBL-LNLD, tMFOCUSS, TMSBL, STBSL, and BSBL-BO methods. These five methods were applied onSensors 2016, 16, 201 12 of 16the MMV problem. For the SVM problem only BSBL-LNLD and BSBL-BO, were applied as the othersare only applicable to the MMV problem. Thus single channels are compressed channel by channel forthe SMV problem, and multiple channels are compressed simultaneously in case of the MMV problem.For the SMV problem, the EEG data is compressed for each vector such that yl “ Axl ,@l “ r1, 2, .., Ls.In case of the MMV problem, the EEG signals were compressed using the vector y “ AvecrXTs. Asshown above the proposed method can sustain good recovery results even at high compression ratese.g., CR 90% (10:1 compression ratio).To the best of our knowledge, these are the best results that have been achieved so far with respectto obtaining high compression rates and low construction errors for the EEG signals in compressedsensing. Not ignoring that fact that the JPEG2000 still achieves the most accurate results at highcompression rates, however, it is not suitable for WBANs.Table 1. NMSE of the different methods.CR 90% 85% 80% 70% 60% 50%Compression ExperimentNMSE (BCIDataSet)BSBL-LNLD (Multichannel) 0.065 0.058 0.016 0.008 0.005 0.002BSBL-BO (Multichannel) 0.094 0.089 0.075 0.014 0.006 0.003BSBL-LNLD (SingleChannel) 0.461 0.384 0.242 0.154 0.094 0.045BSBL-BO (SingleChannel) 0.551 0.414 0.318 0.217 0.134 0.089STBSL-EM 0.791 0.427 0.133 0.038 0.017 0.009TMSBL 0.248 0.178 0.066 0.04 0.022 0.014tMFOCUS 0.665 0.269 0.077 0.035 0.018 0.011NMSE (SeizureDataSet)BSBL-LNLD (Multichannel) 0.242 0.191 0.174 0.114 0.097 0.035BSBL-BO (Multichannel) 0.311 0.257 0.216 0.165 0.114 0.058BSBL-LNLD (SingleChannel) 0.457 0.412 0.35 0.261 0.156 0.098BSBL-BO (SingleChannel) 0.671 0.575 0.472 0.319 0.228 0.147STBSL-EM 0.984 0.728 0.419 0.166 0.091 0.032TMSBL 0.698 0.687 0.217 0.154 0.11 0.036tMFOCUS 0.912 0.757 0.683 0.441 0.098 0.021NMSE (SleepDataSet)BSBL-LNLD (Multichannel) 0.148 0.135 0.095 0.064 0.009 0.004BSBL-BO (Multichannel) 0.176 0.153 0.113 0.094 0.015 0.007BSBL-LNLD (SingleChannel) 0.388 0.265 0.147 0.092 0.058 0.029BSBL-BO (SingleChannel) 0.475 0.356 0.225 0.134 0.075 0.044STBSL-EM 0.89 0.561 0.315 0.126 0.065 0.007TMSBL 0.352 0.243 0.156 0.114 0.072 0.009tMFOCUS 0.864 0.587 0.413 0.324 0.054 0.0174.5. Power Consumption SimulationThe power consumption evaluation of the sensor node is obtained using an open-sourcecycle-accurate Wireless Sensor Network (WSN) simulator called Avrora [35]. It emulates the sensornode circuitry by providing a virtual operating environment of the execution. An emulator softwarefor an Atmel AVR micro-controller sensor platform, Mica2 and MicaZ, is provided in Avrora. It is usedto provide a detailed monitoring evaluation of different behaviors such as packet transmission, energymonitoring, interrupts and stack usage. The energy monitor in Avrora provides the state changes ofthe sensor node and estimates the power consumption of each component of the sensor platform.The compression techniques for the SMV and the MMV problem is implemented in NetworkEmbedded Systems C for TinyOS [36]. TinyOS is an embedded operating system (OS) that provideshardware abstraction for operations such as packet transmission, storage and input/output (I/O). Thecode was compiled and simulated for a MicaZ sensor platform using Avrora. The MicaZ consists of anATMega 128 micro-controller, ZigBee radio, 4 KB of RAM and 128 KB of flash memory. To simplifythe evaluation process, real-time EEG acquisition simulation was not performed. Alternatively, ashort epoch segment of EEG data was loaded into the memory to simulation for a 1 s data window.This simplification does not affect the evaluation results of the power consumption because the EEGsensing is the same across all epochs. The majority of the power on the sensor node is consumedby the micro-controller and the wireless transmitter. Approximately 20% of power consumption isaccounted for the micro-controller, while 70% of the power is accounted for the radio transmitter [37].Sensors 2016, 16, 201 13 of 16For this reason usually the main focus of the power consumption is on the micro-controller and thewireless transmitter.The results of the total power consumption on the sensor node is broken down into the codeexecution of the microcontroller, wireless transmitter radio, and flash memory. The power consumptionof the sensor node of different CR% is estimated over a span of 1 hour of data which is sampled at256 Hz. The battery life is estimated assuming that the battery used is a 3 Volt of 200 mAh. The batterylife is estimated using the following equation: BatteryLi f e “ 0.7 Battery Capacity pmAhqTotal load current { hour pmAq . Theresults is shown in the following Table 2.Table 2. Breakdown of power consumption results at different compression rates in milliwatts.CR MCU Transmitter Memory Total (mW) Battery Life hrs (3V, 200 mAh)0% (NoCompression) 46.14 160.68 0 20.82 3.0450% 20.07 30.67 13.60 64.34 9.7960% 19.24 20.58 13.25 53.07 11.8770% 18.41 14.1 12.91 45.42 13.8780% 17.72 9.65 12.76 40.14 15.6990% 17.04 6.67 12.78 36.49 17.275. Conclusions and Future WorkThis paper proposes a novel method for compressing multi-channel EEG signals usingcompressive sensing. It introduces a way for representing the multi-channel EEG data measurementsas a vector that has a significantly better “block” sparsity structure than the conventional one. The DCTcoefficients of the reorganized vector is shown experimentally to have a high sparse and redundantblock sparse structure. That is the new organization of the data results in a high number of sparseblocks. This enables the state-of art BSBL-BO to produce better reconstruction results of EEG datathat have been compressed using compressive sampling. These results are then further improved bymodifying the BSBL-BO method so that it exploits both the linear and non-linear dependencies in theEEG data and not only the linear one.Previous works have shown that neurophysiological signals (including EEG signals) have linearand non-linear dependencies within each channel and between channels. To find the characteristics ofthe linear and non-linear dependencies in the proposed EEG vector representation, we experimentallycalculate the average correlation and phase locking values of various EEG multi-channel data. Toexploit these dependencies, we propose a modification to the parameters of BSBL-BO. The modifiedBSBL-BO is then applied on the sparse vector that results from our proposed representation of the EEGdata. We show that these modifications enable BSBL-BO to exploit not only the linear dependencybut also the non-linear ones. We also studied the characteristics of the reorganized DCT coefficientsvector. We used correlated and uncorrelated random signals to prove that the sparse structure ofthe reorganized DCT coefficients is reproducible in correlated signals. The redundancy in the blockstructure increases with the increase in the number of correlated channels.The proposed compressed sensing technique is applied on the MMV and the SMV problems.The compressed signals were decompressed using different existing algorithms for comparison. Twodatasets of EEG signals of different patients and a third dataset of brain computer interface data wereused. The results show that, the proposed BSBL-LNLD method results in a significant lower errorcompared to other methods, even at high compression rates such as 10:1. To the best of our knowledge,the results obtained are the best in the (WBAN) literature for EEG signals, JPEG2000 still remains thebest compression technique in terms of accuracy at high compression rates, but it is not suitable forWBANs due to its high power consumption.Sensors 2016, 16, 201 14 of 16Mostly, the sensor architecture of a WBAN framework consists of a sensing part, processingand data transmission parts. Approximately 20% of power consumption is accounted for themicro-controller and the sensor, while 70% of the power is accounted for the radio transmitter [37].The data transmission accounts for the most significant power consumption. That is the reason whyreducing the power consumption of the data transmission has been an interesting topic in previousworks. Therefore, it is essential to transmit fewer amounts of bits to achieve the lowest possibleenergy consumption. This is usually how the sensor’s battery lifetime is extended, provided that therecovery quality is high to achieve a reliable operation in WBAN applications. Only few papers showslittle improvement to solve the power consumption of sensing, processing, and data transmissionpower all together [13–15]. Their work does not achieve high compression rates of the EEG, hencethe recovery quality is too low which is reliable for WBAN applications. This work only solved thedata compression problem achieving high recovery quality at high compression rates. However, it didnot propose a solution for the power consumption of the sensing, processing, and data transmissiontogether. In the future work, we intend to find a solution that accounts for high recovery quality duringhigh data compression (10:1) while optimizing the sensing and processing power as well.Acknowledgments: This work was made possible by NPRP grant # 7-684-1-127 from the Qatar National ResearchFund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Author Contributions: This paper presents the research conducted by Hesham Mahrous, in collaboration withDr. Rabab K. Ward. I hereby declare that I am the first author of this paper. Dr. Ward helped in formulatingthe research problem, supervised the direction of the research, and provided significant editorial comments andimportant suggestions for the organization of each manuscript.Conflicts of Interest: The authors declare no conflict of interest.References1. Zhang, Z.; Jung, T.-P.; Makeig, S.; Rao, B.D. Compressed Sensing of EEG for Wireless Telemonitoringwith Low Energy Consumption and Inexpensive Hardware. IEEE Trans. Biomed. Eng. 2014, 60, 221–224.[CrossRef] [PubMed]2. Aviyente, S. Compressed Sensing Framework for EEG Compression. In Proceedings of the IEEE/SP 14thWorkshop on Statistical Signal Processing, Madison, WI, USA, 26–29 August 2007; pp. 181–184.3. 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eng
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Vancouver : University of British Columbia Library
Multidisciplinary Digital Publishing Institute
CC BY 4.0
Faculty
EEG signals
Tele-monitoring
Compressed sensing
BSBL
Multivariate compression
Linear and nonlinear dependency
Block Sparse Compressed Sensing of Electroencephalogram (EEG) Signals by Exploiting Linear and Non-Linear Dependencies
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