Study of Phase Relationships in ECoG Signals Using Hilbert-Huang Transforms
|
|
- Ronald Mosley
- 5 years ago
- Views:
Transcription
1 Study of Phase Relationships in ECoG Signals Using Hilbert-Huang Transforms Gahangir Hossain, Mark H. Myers, and Robert Kozma Center for Large-Scale Integrated Optimization and Networks (CLION) The University of Memphis, Memphis, TN 38152, USA Abstract. This study investigates phase relationships between electrocorticogram (ECoG) signals through Hilbert-Huang Transform (HHT), combined with Empirical Mode Decomposition (EMD). We perform spatial and temporal filtering of the raw signals, followed by tuning the EMD parameters. It can be seen that carefully tuning of EMD filter, it is possible to capture distinct features of non-stationary data. This makes EMD, combined with HHT a valuable tool of complex brain signal analysis and modeling. Keywords: Electrocorticogram (ECoG), Hilbert Huang Transform (HHT), Empirical Mode Decomposition (EMD); Phase cone. 1 Introduction Hilbert-Huang transform (HHT) is a recent method which generates amplitude and frequency vs. time spectra using a powerful data analysis tool called empirical mode decomposition (EMD) [1, 2]. HHT is suitable to analyze non-stationary and nonlinear data. Global basis states must be replaced with adaptive, locally determined ones, a process the first stage of the HHT does perform. The resulting basis states are, in general, not strictly orthogonal. The goal of this study is to analysis different phase relationships in phase cone discovery from different types of EMD filtered datasets. The main idea behind EMD approach is to first compute the local median of a signal via a sifting procedure and then subtract the local median of a signal before applying the amplitude spectrum method to define instantaneous frequency. Therefore, in performance comparison of EMD filtering the very recent discovery of Hou and Shi, EMD performance depends on the sensitivity on the number of sifting and the stopping criteria [3, 4], is adopted. The variance of EMD filtering is performed by carefully tuning some dependent parameters in intrinsic mode function (IMF) that decomposes the signal into modes that are intrinsic to the function using an iterative or sifting process considering only local extrema. In ECoG analysis, the spatially ordered phase relationship between cortical signals is named as phase cone [5]. Instantaneous identification of phase cones therefore serves as markers by which to locate emergent AM patterns at varying latencies over sequential trials. To identify better phase cones, ECoG, data must be preprocessed
2 without changing its inherent properties. The present work aims at studying phase relationships in ECoG data to improve the identification of salient properties of spatio-temporal brain dynamics. This work starts with a brief introduction to the applied methodology. This is followed by describing the analyzed data obtained from intracranial experiments with chronically implanted rabbits. We study the performance of HHT processing algorithms and optimize parameters of EMD algorithm. Finally, we summarize the obtained results and conclude direction for future studies. 2 Background Study ECoG represents complex irregular brain signals by recording of tiny electrical potentials that underlie neural activities related to perception and action. This section provides some background materials, mostly fast Fourier transform and Hilbert- Huang transformation that are used to uncover phase cones from ECoG signals. The implemented signal processing approach is explained on the block diagram as shown in Figure 1. After a very brief review of the HHT, and Phase cone identification, the comparative behavior of these two transforms on various filtered data set is explored. Along this direction the IMF parameter number-of-sifting in iteration is varied. Fig. 1. Block Diagram of Experimental Steps 2.1 Hilbert Transformations For an arbitrary signal v, the analytic signal V(t) is a complex function of time defined as: 1 v(t') + v'(t) = PV π (t t') dt' (1)
3 where PV corresponds to the Cauchy Principal Value. At each digitizing step, the time series yielded a point in the complex plane [vj (t)]. Each signal denoted by v(t) was transformed to a vector, V(t) having a real part, v(t), and an imaginary part, v'(t). As seen from Eq. (1), the Hilbert transform v (t) of v(t) can be considered as the convolution of the function v(t) with 1/πt. Each ECoG signal denoted v(t) was transformed to a vector, V(t), having a real part, v(t), and an imaginary part, v'(t) (Freeman, Rogers, 2002): V(t) = v(t) + i v'(t) = AA(t) exp [iap(t)] (2) where the length of the vector gave the analytic amplitude, AA(t) = [ v 2 (t) + v' 2 (t) ].5 (3) and arc tangent of the vector gave the analytic phase, AP(t) = atan [ v'(t) / v(t) ]. (4) The instantaneous amplitude AA(t) and the instantaneous phase AP(t) of the signal v(t) are thus uniquely defined by Eqs. (3,4). The real data corresponds to the raw incoming data, while the Hilbert transform (HT) provides the imaginary frequency that is changing in time. The imaginary part is a version of the original real sequence with a 90 phase shift. Sin functions are therefore transformed to cosines and vice versa [5]. The Hilbert transformed series has the same amplitude and frequency content as the original real data and includes phase information that depends on the phase of the original data. The Hilbert transform is useful in calculating instantaneous attributes of a time series, especially the amplitude and frequency. The instantaneous amplitude is the amplitude of the complex Hilbert transform; the instantaneous frequency is the time rate of change of the instantaneous phase angle [5]. For a pure sinusoid, the instantaneous amplitude and frequency are constant. 2.2 The Hilbert-Huang Transform The Hilbert-Huang transform is the combination of empirical mode decomposition (EMD) and Hilbert transform (HT). EMD process deconstructs the signal into a set of intrinsic mode functions (IMF) and HT extracts frequency vs. time information from each of the IMF s. The EMD is a method of signal decomposition introduced for analysis of nonlinear and non-stationary signals. It is to identify proper time scales that reveal physical characteristics of the signals, and then decomposed the signal into modes that are intrinsic to the function, referred as Intrinsic Mode Functions (IMFs). IMFs interpret signals as the zero mean oscillations at each scale and the local mean of the signal respectively. IMFs are signals satisfying two conditions: (a) in the whole dataset, the number of extrema and the number of zero-crossings must either be equal or differ at
4 most by one, and (b) at any point, the mean value of the envelope defined by local maxima and the envelope defined by the local minima is zero. Condition one is similar to the traditional narrow band requirements for a stationary Gaussian process. Whereas, the second condition is necessary in order to avoid unwanted fluctuations induced by asymmetric waveforms in the instantaneous frequencies will not have. An IMF is not limited as a sinusoid in the classical sense (such as in Fourier Transforms), it can be an amplitude and frequency modulated signal and, can even be a non-stationary signal. This method enables us to eliminate the drawback of a traditional time-domain to frequency-domain transformation (like Fourier transform) where frequency contents are observed by sacrificing time resolution. Instead, IMFs provide amplitude and frequency information of a signal at any given time. Practically, EMD is implemented as an iterative or sifting process considering only local extrema. The EMD algorithm for amplitude and frequency extraction from a given discrete IMF is shown in Table 1. Table 1. EMD Algorithm (sifting algorithm) Given a discretely sampled signal y(t), Step-1: Find the locations of all the extrema of y(t) first IMF signal. Step-2: Interpolate between all the minima (respectively maxima) to obtain the lower signal envelope, ymin(t) (respectively ymax(t)). Step-3: Compute the local mean m(t) = [ymin(t) + ymax(t)]/2. Step-4: Subtract the mean from the signal to obtain the oscillatory mode d(t)= y(t) m(t) // removing the trend Step-5: If d(t) meets stopping criteria, then define c i (t)=d(t) and i = i +1 // increment i r(t)= y(t) d(t) // extract the residual If d(t) does not meets stopping criteria farther sifting is required. set y(t)=d(t) and repeat from step 1. Step-6: Repeat steps 1 through 5 until the residual no longer contains any useful frequency information. Amplitudes and frequencies are extracted from these IMF s in the second stage of the HHT process. The instantaneous amplitude and angular frequency associated with each IMF depend on the amplitude and phase of a complex number that the IMF and its Hilbert transform (HT) define. The real part of the complex number is the IMF; the imaginary part of the number is the IMF s HT. The instantaneous amplitude is the amplitude of this complex number. The instantaneous angular frequency associated with that IMF is the derivative of the unwrapped phase. The entire process is repeated for each IMF to extract the complete frequency versus time information from the original ECoG data set. The computation of the HT is essentially a convolution of an IMF, x(t), with 1=t and effective to emphasize the local properties of x(t). This locality preserves the time structure of the signal s amplitude and frequency. Generally, ECoG signals are represented equal to the sum of its parts. We have N
5 IMFs and a final residual rn (t), (5) The second stage of the HHT process extracts the amplitude and frequency information from each IMF (HT algorithm in Table 2). Table 2. Algorithm: HT Given a discretely sampled signal y(t), Step-1: Compute the IMF s discrete Fourier transform (DFT) using the series expression (1) for the transform. Step-2: Compute the HT. Use the real and imaginary parts of step 1 s DFT as coefficients (M = N/2): Step-3: Form the complex number z j = x j +iy j, extract the phase j = tan -1 (y j /x j ). Step-4: Unwrap the phase so that it becomes a monotonically increasing function. Step-5: Determine the frequency. Take the derivative of the phase Step-6: Determine the amplitude. 2.3 Phase Cone Detection Phase cones describe the spatially ordered phase relationship between cortical signals. Phase cones reveals the property: a state transition is not everywhere instantaneous but begins at a site of nucleation and spreads concentrically, like the formation of a snowflake around a grain of dust. Fig. 2. A 3D plot of a special distribution of analytic phase across 8 x 8 ECoG electrode array at the time frame t = s. The phase lags confirm as like phase cone.
6 The apex that marks the site in the cortex is a random variable both in sign (lead or lag) and location. The cones can appear with positive and negative phase lags, corresponding to explosive and implosive transitions in the cortical spatiotemporal dynamics and there can be several phase cones simultaneously present in a measurement window [6,7]. Phase Cones depicting an "implosion" due to a phase lag of the ECoG time series. The power spectral analysis usually reveals a dominant peak at a central frequency in short segments and near 1/f power spectra in time segment > 1sec. This dominant component of each burst is constituted by the power at the point where the central frequency rises and it drops towards the end in temporal amplitude modulation (AM) on all channels [8,9]. These AM pattern have been found to be accompanied by pattern of phase modulation and forms like a cone, that is named as phase cone. More details on identification of propagating phase gradients in ECoG signals using Dynamic Logic (DL) approach is experimented [10]. Figure 2 shows 3D view of sample phase cones at timestamp t128. The figure explains special distribution of analytical phase across the 8 x 8 ECoG electrode array at that time frame. 3 Data and Methods 3.1 Data To demonstrate the comparative study 64-channel ECoG recordings of rabbit data is used. The data is captured in Walter Freeman's UC Berkeley lab [11,12]. ECoG was recorded monopolarly with respect to that cranial reference electrode nearest the array and amplified by fixed-gain (10K) WPI ISO 4/8 differential amplifiers. Each channel was filtered with single pole, first order analog filters set at 100 Hz and 0.1 Hz. Records of sixty-four 12-bit samples multiplexed at 10 µs were recorded at a 2 ms digitizing interval (500 Hz) for 6 seconds and stored as signed-16-bit integers. More specifically, a sample data is a 64 x 3000 matrix, which means 64 ECoG channels measured for 6s, at 2 ms sampling time, so in total 3000 points. The incremental time delay caused by multiplexing of the ECoG was corrected off-line. Bad channels associated with movement artifact or EMG were identified by visual editing and replaced off-line by averaging the signals of two adjacent channels. Using the EMD technique described in previous section and changing the value of number-of-sifting in IMF iteration, fix different types of filtered data sets are created. 3.2 Methods The comparative experimental procedure works as the block diagram shown in Figure 1. The filtering based system works for three steps. In first step, input ECoG signals are decomposed using EMD with couple of iterations for all signals band to be stabilized in terms of IMFs. After successful IMF iterations EMD phase ended. In step two, HT is applied on the filtered signal. That works as HHT on the applied signal. Tuning the EMD parameter finally tuned the HHT for different dataset. In farther step the reflection of amplitude and phase change is analyzed for meaningful pattern discovery. As ECoG time series are large in size, a significant sample
7 selection is a crucial part of the comparative study. To select a good and representative sample window average phase around 60 Hz for all 64 channels (8 X 8) are analyzed for different filtered dataset. An average phase spectrum of all 64 channels for different filtered dataset is plotted. By visual inspection on the figure matrix any filtered data is compared with the top row. 4 Results In this section three important properties of Hilbert transform namely analytic amplitude, analytic phase and unwrapped phase are plotted from all EMD filtered ECoG signal. Fig. 3. Hilbert analytic amplitude and phase comparison between original data set and EMD filtered datasets.
8 The original rabbit data is marked as not filtered (NF) whereas EMD filtered data sets are varied based on number of IMF sifting. These versions are marked as IMFs10, IMFs20, IMFs50, IMFs100, IMFs500, IMFs600, IMFs700, IMFs800, IMFs900 and IMFs1000.Hilbert analytic phase and amplitude comparison are shown in Figure 3. Fig.4. Hilbert unwrap phase comparison between original data set and EMD filtered datasets. Smoothing of the time series occurs in the analytic amplitude due to an increased EMD order. A change in the phase/frequency range from higher order EMD filtering seems to increase the number of phases over time. In Figure 4, the unwrapped phase changes dramatically between higher order EMD filtering. The challenge of filter selection may cause bifurcations of the unwrapped phase to occur due to noise and opposed to too much filtering which may cause important attributes of the signal to become smoothed over. An EMD order of less than 100 seems to enable the ECoG signal to retain its lower frequency attributes while discarding noisy artifacts. 5. Conclusion This study revealed that combination of EMD and HT is better in lower-order filtering in ECoG analysis mostly tuning the filtering parameters related to sifting in IMF iteration less than hundred times. Hence, EMD may be a useful and effective tool for filtering ECoG data before phase cone detection. Analysis of other EMD parameters (e.g. fidelity or noise related issues) for filter tuning can be experimented in the next phase. Standard EMD is limited to the analysis of single data channels, whereas modern applications require its multichannel extensions. The complex ECoG signal needs to
9 be processed using multivariate algorithms to get complex valued IMFs. In that case,, a bi-variate or tri-variate EMD algorithm may be useful. On the other hand, results from the EMD with HT (HHT) method showed relatively deprived phase behavior that need more inspection in further study. Additionally, EMD parameters that capture temporal information changes need a continuous, automatic assessment in phase gradient identification process. Therefore, a dynamic process of optimal approximation and robust identification of phase cones from filtered or noisy data can be experimented. Future work includes better phase cone detection mechanism, automation of the detection process, and performance improvement and analysis of the impact of filtering on phase relationship. Acknowledgments: This work has been supported in part by the University of Memphis Foundation, through a grant of the FedEx Institute of Technology. References 1. B en dat J. S.: The Hilbert Transform and Applications to Correlation Measurements, Bruel & Kjiaer, Denmark, Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C. and Liu, H. H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Proc. Roy. Soc. Lond. A, 1998, pp Norden E. Huang et. al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proc. R. Soc. Lond. A., vol. 454, pp , Battista, B., Knapp, C., McGee, T. and Goebel. V.,Application of the Empirical Mode Decomposition and Hilbert-Huang Transform to Seismic Reflection Data. Geophysics, Vol.72, No.2, pages H29-H37., N. 5. Freeman WJ, Rogers LJ.,"Fine temporal resolution of analytic phase reveals episodic synchronization by state transitions in gamma EEGs",J Neurophysiol Feb;87(2): Barrie, J.M.,Freeman,W.J.& Lenhart,M.D. (1996), Spatiotemporal Analysis of Prepyriform, Visual, Auditory, and Somesthetic Surface EEGs in Trained Rabbits, J. Neurophysiology, Vol. 76, No. 1, pp Freeman W. J.,and Barrie, J. M., Analysis of Spatial Patterns of Phase in Neocortical Gamma EEGs in Rabbit, Journal of Neurophysiology 84: , Freeman, W. J., Origin, structure, and role of background EEG activity, Part II, Analytic Phase, 2004, Clinical Neurophysiology, Freeman, W. J., Origin, structure, and role of background EEG activity, Part I, Analytic Amplitude, Clinical Neurophysiology (2005) 116 (5): Kozma, R., W.J. Freeman (2001) Analysis of Visual Theta Rhythm Experimental and Theoretical Evidence of Visual Sniffing, IEEE/INNS Int. Joint Conf. Neural Networks, Washington D.C., July 14-19, 2001, pp Freeman, W.J., Mass Action in the Nervous System. Academic Press, New York, Kozma, R.; Perlovsky, L.; Ankishetty, J.S., "Detection of propagating phase gradients in EEG signals using Model Field Theory of non-gaussian mixtures," Neural Networks, IJCNN (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, vol., no., pp
Empirical Mode Decomposition: Theory & Applications
International Journal of Electronic and Electrical Engineering. ISSN 0974-2174 Volume 7, Number 8 (2014), pp. 873-878 International Research Publication House http://www.irphouse.com Empirical Mode Decomposition:
More informationEnsemble Empirical Mode Decomposition: An adaptive method for noise reduction
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735. Volume 5, Issue 5 (Mar. - Apr. 213), PP 6-65 Ensemble Empirical Mode Decomposition: An adaptive
More informationKONKANI SPEECH RECOGNITION USING HILBERT-HUANG TRANSFORM
KONKANI SPEECH RECOGNITION USING HILBERT-HUANG TRANSFORM Shruthi S Prabhu 1, Nayana C G 2, Ashwini B N 3, Dr. Parameshachari B D 4 Assistant Professor, Department of Telecommunication Engineering, GSSSIETW,
More informationApplication of Hilbert-Huang Transform in the Field of Power Quality Events Analysis Manish Kumar Saini 1 and Komal Dhamija 2 1,2
Application of Hilbert-Huang Transform in the Field of Power Quality Events Analysis Manish Kumar Saini 1 and Komal Dhamija 2 1,2 Department of Electrical Engineering, Deenbandhu Chhotu Ram University
More informationEmpirical Mode Decomposition (EMD) of Turner Valley Airborne Gravity Data in the Foothills of Alberta, Canada
Empirical Mode Decomposition (EMD) of Turner Valley Airborne Gravity Data in the Foothills of Alberta, Canada Hassan Hassan* GEDCO, Calgary, Alberta, Canada hassan@gedco.com Abstract Summary Growing interest
More informationEmpirical Mode Decomposition (EMD) of Turner Valley Airborne Gravity Data in the Foothills of Alberta, Canada*
Empirical Mode Decomposition (EMD) of Turner Valley Airborne Gravity Data in the Foothills of Alberta, Canada* Hassan Hassan 1 Search and Discovery Article #41581 (2015)** Posted February 23, 2015 *Adapted
More informationRandom and coherent noise attenuation by empirical mode decomposition Maïza Bekara, PGS, and Mirko van der Baan, University of Leeds
Random and coherent noise attenuation by empirical mode decomposition Maïza Bekara, PGS, and Mirko van der Baan, University of Leeds SUMMARY This paper proposes a new filtering technique for random and
More informationAtmospheric Signal Processing. using Wavelets and HHT
Journal of Computations & Modelling, vol.1, no.1, 2011, 17-30 ISSN: 1792-7625 (print), 1792-8850 (online) International Scientific Press, 2011 Atmospheric Signal Processing using Wavelets and HHT N. Padmaja
More informationI-Hao Hsiao, Chun-Tang Chao*, and Chi-Jo Wang (2016). A HHT-Based Music Synthesizer. Intelligent Technologies and Engineering Systems, Lecture Notes
I-Hao Hsiao, Chun-Tang Chao*, and Chi-Jo Wang (2016). A HHT-Based Music Synthesizer. Intelligent Technologies and Engineering Systems, Lecture Notes in Electrical Engineering (LNEE), Vol.345, pp.523-528.
More informationHilbert-Huang Transform, its features and application to the audio signal Ing.Michal Verner
Hilbert-Huang Transform, its features and application to the audio signal Ing.Michal Verner Abstrakt: Hilbert-Huangova transformace (HHT) je nová metoda vhodná pro zpracování a analýzu signálů; zejména
More informationSIMULATING RESTING CORTICAL BACKGROUND ACTIVITY WITH FILTERED NOISE. Journal of Integrative Neuroscience 7(3):
SIMULATING RESTING CORTICAL BACKGROUND ACTIVITY WITH FILTERED NOISE Journal of Integrative Neuroscience 7(3): 337-344. WALTER J FREEMAN Department of Molecular and Cell Biology, Donner 101 University of
More informationGuan, L, Gu, F, Shao, Y, Fazenda, BM and Ball, A
Gearbox fault diagnosis under different operating conditions based on time synchronous average and ensemble empirical mode decomposition Guan, L, Gu, F, Shao, Y, Fazenda, BM and Ball, A Title Authors Type
More informationSensitivity Analysis of Hilbert Transform with Band- Pass FIR Filters for Robust Brain Computer Interface
Sensitivity Analysis of Hilbert Transform with Band- Pass FIR Filters for Robust Brain Computer Interface Jeffery Jonathan (Joshua) Davis (1,2) 1 Center for Large-Scale Integrated Optimization Networks
More informationSUMMARY THEORY. VMD vs. EMD
Seismic Denoising Using Thresholded Adaptive Signal Decomposition Fangyu Li, University of Oklahoma; Sumit Verma, University of Texas Permian Basin; Pan Deng, University of Houston; Jie Qi, and Kurt J.
More information21/01/2014. Fundamentals of the analysis of neuronal oscillations. Separating sources
21/1/214 Separating sources Fundamentals of the analysis of neuronal oscillations Robert Oostenveld Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, The Netherlands Use
More informationON THE RELATIONSHIP BETWEEN INSTANTANEOUS FREQUENCY AND PITCH IN. 1 Introduction. Zied Mnasri 1, Hamid Amiri 1
ON THE RELATIONSHIP BETWEEN INSTANTANEOUS FREQUENCY AND PITCH IN SPEECH SIGNALS Zied Mnasri 1, Hamid Amiri 1 1 Electrical engineering dept, National School of Engineering in Tunis, University Tunis El
More informationINDUCTION MOTOR MULTI-FAULT ANALYSIS BASED ON INTRINSIC MODE FUNCTIONS IN HILBERT-HUANG TRANSFORM
ASME 2009 International Design Engineering Technical Conferences (IDETC) & Computers and Information in Engineering Conference (CIE) August 30 - September 2, 2009, San Diego, CA, USA INDUCTION MOTOR MULTI-FAULT
More informationAssessment of Power Quality Events by Empirical Mode Decomposition based Neural Network
Proceedings of the World Congress on Engineering Vol II WCE, July 4-6,, London, U.K. Assessment of Power Quality Events by Empirical Mode Decomposition based Neural Network M Manjula, A V R S Sarma, Member,
More informationASSESSMENT OF POWER QUALITY EVENTS BY HILBERT TRANSFORM BASED NEURAL NETWORK. Shyama Sundar Padhi
ASSESSMENT OF POWER QUALITY EVENTS BY HILBERT TRANSFORM BASED NEURAL NETWORK Shyama Sundar Padhi Department of Electrical Engineering National Institute of Technology Rourkela May 215 ASSESSMENT OF POWER
More informationTelemetry Vibration Signal Trend Extraction Based on Multi-scale Least Square Algorithm Feng GUO
nd International Conference on Electronics, Networ and Computer Engineering (ICENCE 6) Telemetry Vibration Signal Extraction Based on Multi-scale Square Algorithm Feng GUO PLA 955 Unit 9, Liaoning Dalian,
More informationEMD Approach to Multichannel EEG Data - The Amplitude and Phase Synchrony Analysis Technique
EMD Approach to Multichannel EEG Data - The Amplitude and Phase Synchrony Analysis Technique Tomasz M. Rutkowski 1, Danilo P. Mandic 2, Andrzej Cichocki 1, and Andrzej W. Przybyszewski 3,4 1 Laboratory
More information(i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods
Tools and Applications Chapter Intended Learning Outcomes: (i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods
More informationFrequency Demodulation Analysis of Mine Reducer Vibration Signal
International Journal of Mineral Processing and Extractive Metallurgy 2018; 3(2): 23-28 http://www.sciencepublishinggroup.com/j/ijmpem doi: 10.11648/j.ijmpem.20180302.12 ISSN: 2575-1840 (Print); ISSN:
More informationLarge-scale cortical correlation structure of spontaneous oscillatory activity
Supplementary Information Large-scale cortical correlation structure of spontaneous oscillatory activity Joerg F. Hipp 1,2, David J. Hawellek 1, Maurizio Corbetta 3, Markus Siegel 2 & Andreas K. Engel
More informationMethod for Mode Mixing Separation in Empirical Mode Decomposition
1 Method for Mode Mixing Separation in Empirical Mode Decomposition Olav B. Fosso*, Senior Member, IEEE, Marta Molinas*, Member, IEEE, arxiv:1709.05547v1 [stat.me] 16 Sep 2017 Abstract The Empirical Mode
More informationEE 791 EEG-5 Measures of EEG Dynamic Properties
EE 791 EEG-5 Measures of EEG Dynamic Properties Computer analysis of EEG EEG scientists must be especially wary of mathematics in search of applications after all the number of ways to transform data is
More informationBiomedical Signals. Signals and Images in Medicine Dr Nabeel Anwar
Biomedical Signals Signals and Images in Medicine Dr Nabeel Anwar Noise Removal: Time Domain Techniques 1. Synchronized Averaging (covered in lecture 1) 2. Moving Average Filters (today s topic) 3. Derivative
More informationAdaptive Fourier Decomposition Approach to ECG Denoising. Ze Wang. Bachelor of Science in Electrical and Electronics Engineering
Adaptive Fourier Decomposition Approach to ECG Denoising by Ze Wang Final Year Project Report submitted in partial fulfillment of the requirements for the Degree of Bachelor of Science in Electrical and
More informationNOISE CORRUPTION OF EMPIRICAL MODE DECOMPOSITION AND ITS EFFECT ON INSTANTANEOUS FREQUENCY
Advances in Adaptive Data Analysis Vol., No. 3 (1) 373 396 c World Scientific Publishing Company DOI: 1.114/S179353691537 NOISE CORRUPTION OF EMPIRICAL MODE DECOMPOSITION AND ITS EFFECT ON INSTANTANEOUS
More informationChapter 4 SPEECH ENHANCEMENT
44 Chapter 4 SPEECH ENHANCEMENT 4.1 INTRODUCTION: Enhancement is defined as improvement in the value or Quality of something. Speech enhancement is defined as the improvement in intelligibility and/or
More informationAdaBoost based EMD as a De-Noising Technique in Time Delay Estimation Application
International Journal of Computer Applications (975 8887) Volume 78 No.12, September 213 AdaBoost based EMD as a De-Noising Technique in Time Delay Estimation Application Kusma Kumari Cheepurupalli Dept.
More informationFeature Extraction of ECG Signal Using HHT Algorithm
International Journal of Engineering Trends and Technology (IJETT) Volume 8 Number 8- Feb 24 Feature Extraction of ECG Signal Using HHT Algorithm Neha Soorma M.TECH (DC) SSSIST, Sehore, M.P.,India Mukesh
More informationEnhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis
Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis Mohini Avatade & S.L. Sahare Electronics & Telecommunication Department, Cummins
More informationPattern Recognition Part 2: Noise Suppression
Pattern Recognition Part 2: Noise Suppression Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Electrical Engineering and Information Engineering Digital Signal Processing
More informationHilbert-Huang Transform and Its Applications in Engineering and Biomedical Signal Analysis
Hilbert-Huang Transform and Its Applications in Engineering and Biomedical Signal Analysis MILAN STORK Dept. of Applied Electronics and Telecommunications/RICE Faculty of Electrical Engineering, University
More informationThe Application of the Hilbert-Huang Transform in Through-wall Life Detection with UWB Impulse Radar
PIERS ONLINE, VOL. 6, NO. 7, 2010 695 The Application of the Hilbert-Huang Transform in Through-wall Life Detection with UWB Impulse Radar Zijian Liu 1, Lanbo Liu 1, 2, and Benjamin Barrowes 2 1 School
More informationICA & Wavelet as a Method for Speech Signal Denoising
ICA & Wavelet as a Method for Speech Signal Denoising Ms. Niti Gupta 1 and Dr. Poonam Bansal 2 International Journal of Latest Trends in Engineering and Technology Vol.(7)Issue(3), pp. 035 041 DOI: http://dx.doi.org/10.21172/1.73.505
More informationIntroduction. Chapter Time-Varying Signals
Chapter 1 1.1 Time-Varying Signals Time-varying signals are commonly observed in the laboratory as well as many other applied settings. Consider, for example, the voltage level that is present at a specific
More informationANALYSIS OF POWER SYSTEM LOW FREQUENCY OSCILLATION WITH EMPIRICAL MODE DECOMPOSITION
Journal of Marine Science and Technology, Vol., No., pp. 77- () 77 DOI:.9/JMST._(). ANALYSIS OF POWER SYSTEM LOW FREQUENCY OSCILLATION WITH EMPIRICAL MODE DECOMPOSITION Chia-Liang Lu, Chia-Yu Hsu, and
More informationInvestigation on Fault Detection for Split Torque Gearbox Using Acoustic Emission and Vibration Signals
Investigation on Fault Detection for Split Torque Gearbox Using Acoustic Emission and Vibration Signals Ruoyu Li 1, David He 1, and Eric Bechhoefer 1 Department of Mechanical & Industrial Engineering The
More informationThe study of Interferogram denoising method Based on Empirical Mode Decomposition
www.ijcsi.org 750 The study of Interferogram denoising method Based on Empirical Mode Decomposition Changun Huang 1, 2, Jiming Guo 3, Xiaodong Yu 4 and Changzheng Yuan 5 1 School of Geodesy and Geomatics,
More informationThe Improved Algorithm of the EMD Decomposition Based on Cubic Spline Interpolation
Signal Processing Research (SPR) Volume 4, 15 doi: 1.14355/spr.15.4.11 www.seipub.org/spr The Improved Algorithm of the EMD Decomposition Based on Cubic Spline Interpolation Zhengkun Liu *1, Ze Zhang *1
More informationBy Shilpa R & Dr. P S Puttaswamy Vidya Vardhaka College of Engineering, India
Global Journal of Researches in Engineering: F Electrical and Electronics Engineering Volume 15 Issue 4 Version 1.0 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals
More informationEvoked Potentials (EPs)
EVOKED POTENTIALS Evoked Potentials (EPs) Event-related brain activity where the stimulus is usually of sensory origin. Acquired with conventional EEG electrodes. Time-synchronized = time interval from
More informationDevelopment of a New Signal Processing Diagnostic Tool for Vibration Signals Acquired in Transient Conditions
A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 33, 213 Guest Editors: Enrico Zio, Piero Baraldi Copyright 213, AIDIC Servizi S.r.l., ISBN 978-88-9568-24-2; ISSN 1974-9791 The Italian Association
More informationSignal segmentation and waveform characterization. Biosignal processing, S Autumn 2012
Signal segmentation and waveform characterization Biosignal processing, 5173S Autumn 01 Short-time analysis of signals Signal statistics may vary in time: nonstationary how to compute signal characterizations?
More informationBaseline wander Removal in ECG using an efficient method of EMD in combination with wavelet
IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 4, Issue, Ver. III (Mar-Apr. 014), PP 76-81 e-issn: 319 400, p-issn No. : 319 4197 Baseline wander Removal in ECG using an efficient method
More informationDiagnosis of root cause for oscillations in closed-loop chemical process systems
Diagnosis of root cause for oscillations in closed-loop chemical process systems Babji Srinivasan Ulaganathan Nallasivam Raghunathan Rengaswamy Department of Chemical Engineering, Texas Tech University,
More informationResearch Article Subband DCT and EMD Based Hybrid Soft Thresholding for Speech Enhancement
Advances in Acoustics and Vibration, Article ID 755, 11 pages http://dx.doi.org/1.1155/1/755 Research Article Subband DCT and EMD Based Hybrid Soft Thresholding for Speech Enhancement Erhan Deger, 1 Md.
More informationIntroduction to Wavelet Transform. Chapter 7 Instructor: Hossein Pourghassem
Introduction to Wavelet Transform Chapter 7 Instructor: Hossein Pourghassem Introduction Most of the signals in practice, are TIME-DOMAIN signals in their raw format. It means that measured signal is a
More informationHHT Sifting and Adaptive Filtering
INSTITUTE FOR DEFENSE ANALYSES HHT Sifting and Adaptive Filtering Reginald N. Meeson August 2003 Approved for public release; distribution unlimited. IDA Paper P-3766 Log: H 03-000428 This work was conducted
More informationKate Allstadt s final project for ESS522 June 10, The Hilbert transform is the convolution of the function f(t) with the kernel (- πt) - 1.
Hilbert Transforms Signal envelopes, Instantaneous amplitude and instantaneous frequency! Kate Allstadt s final project for ESS522 June 10, 2010 The Hilbert transform is a useful way of looking at an evenly
More informationGearbox fault detection using a new denoising method based on ensemble empirical mode decomposition and FFT
Gearbox fault detection using a new denoising method based on ensemble empirical mode decomposition and FFT Hafida MAHGOUN, Rais.Elhadi BEKKA and Ahmed FELKAOUI Laboratory of applied precision mechanics
More informationChapter 2 Direct-Sequence Systems
Chapter 2 Direct-Sequence Systems A spread-spectrum signal is one with an extra modulation that expands the signal bandwidth greatly beyond what is required by the underlying coded-data modulation. Spread-spectrum
More information(Time )Frequency Analysis of EEG Waveforms
(Time )Frequency Analysis of EEG Waveforms Niko Busch Charité University Medicine Berlin; Berlin School of Mind and Brain niko.busch@charite.de niko.busch@charite.de 1 / 23 From ERP waveforms to waves
More informationNoise Reduction in Cochlear Implant using Empirical Mode Decomposition
Science Arena Publications Specialty Journal of Electronic and Computer Sciences Available online at www.sciarena.com 2016, Vol, 2 (1): 56-60 Noise Reduction in Cochlear Implant using Empirical Mode Decomposition
More informationImpact of Time Varying Angular Frequency on the Separation of Instantaneous Power Components in Stand-alone Power Systems
Impact of Time Varying Angular Frequency on the Separation of Instantaneous Power Components in Stand-alone Power Systems Benedikt Hillenbrand *, Geir Kulia **, and Marta Molinas *** * Department of Electric
More informationA Novel Approach to Improve the Smoothening the Wind Profiler Doppler Spectra Using Empirical Mode Decomposition with Moving Average Method
A Novel Approach to Improve the Smoothening the Wind Profiler Doppler Spectra Using Empirical Mode Decomposition with Moving Average Method S. Vamsee Krishna 1, V. Mahesh 2, P. Krishna Murthy 3, Dr. V.
More informationNon-stationary Analysis/Synthesis using Spectrum Peak Shape Distortion, Phase and Reassignment
Non-stationary Analysis/Synthesis using Spectrum Peak Shape Distortion, Phase Reassignment Geoffroy Peeters, Xavier Rodet Ircam - Centre Georges-Pompidou, Analysis/Synthesis Team, 1, pl. Igor Stravinsky,
More informationTRANSFORMS / WAVELETS
RANSFORMS / WAVELES ransform Analysis Signal processing using a transform analysis for calculations is a technique used to simplify or accelerate problem solution. For example, instead of dividing two
More informationDetecting spread spectrum pseudo random noise tags in EEG/MEG using a structure-based decomposition
Detecting spread spectrum pseudo random noise tags in EEG/MEG using a structure-based decomposition P Desain 1, J Farquhar 1,2, J Blankespoor 1, S Gielen 2 1 Music Mind Machine Nijmegen Inst for Cognition
More informationDetermination of instants of significant excitation in speech using Hilbert envelope and group delay function
Determination of instants of significant excitation in speech using Hilbert envelope and group delay function by K. Sreenivasa Rao, S. R. M. Prasanna, B.Yegnanarayana in IEEE Signal Processing Letters,
More informationChapter 2 Channel Equalization
Chapter 2 Channel Equalization 2.1 Introduction In wireless communication systems signal experiences distortion due to fading [17]. As signal propagates, it follows multiple paths between transmitter and
More informationCHAPTER 6 INTRODUCTION TO SYSTEM IDENTIFICATION
CHAPTER 6 INTRODUCTION TO SYSTEM IDENTIFICATION Broadly speaking, system identification is the art and science of using measurements obtained from a system to characterize the system. The characterization
More informationMode shape reconstruction of an impulse excited structure using continuous scanning laser Doppler vibrometer and empirical mode decomposition
REVIEW OF SCIENTIFIC INSTRUMENTS 79, 075103 2008 Mode shape reconstruction of an impulse excited structure using continuous scanning laser Doppler vibrometer and empirical mode decomposition Yongsoo Kyong,
More informationApplication of the Empirical Mode Decomposition and Hilbert-Huang Transform to Seismic Reflection Data
University of South Carolina Scholar Commons Faculty Publications Earth and Ocean Sciences, Department of --27 Application of the Empirical Mode Decomposition and Hilbert-Huang Transform to Seismic Reflection
More informationA Review of SSVEP Decompostion using EMD for Steering Control of a Car
A Review of SSVEP Decompostion using EMD for Steering Control of a Car Mahida Ankur H 1, S. B. Somani 2 1,2. MIT College of Engineering, Kothrud, Pune, India Abstract- Recently the EEG based systems have
More information2.1 BASIC CONCEPTS Basic Operations on Signals Time Shifting. Figure 2.2 Time shifting of a signal. Time Reversal.
1 2.1 BASIC CONCEPTS 2.1.1 Basic Operations on Signals Time Shifting. Figure 2.2 Time shifting of a signal. Time Reversal. 2 Time Scaling. Figure 2.4 Time scaling of a signal. 2.1.2 Classification of Signals
More informationA Faster Method for Accurate Spectral Testing without Requiring Coherent Sampling
A Faster Method for Accurate Spectral Testing without Requiring Coherent Sampling Minshun Wu 1,2, Degang Chen 2 1 Xi an Jiaotong University, Xi an, P. R. China 2 Iowa State University, Ames, IA, USA Abstract
More informationWavelet Transform for Bearing Faults Diagnosis
Wavelet Transform for Bearing Faults Diagnosis H. Bendjama and S. Bouhouche Welding and NDT research centre (CSC) Cheraga, Algeria hocine_bendjama@yahoo.fr A.k. Moussaoui Laboratory of electrical engineering
More informationFourier and Wavelets
Fourier and Wavelets Why do we need a Transform? Fourier Transform and the short term Fourier (STFT) Heisenberg Uncertainty Principle The continues Wavelet Transform Discrete Wavelet Transform Wavelets
More informationRandom noise attenuation using f-x regularized nonstationary autoregression a
Random noise attenuation using f-x regularized nonstationary autoregression a a Published in Geophysics, 77, no. 2, V61-V69, (2012) Guochang Liu 1, Xiaohong Chen 1, Jing Du 2, Kailong Wu 1 ABSTRACT We
More informationA Novel Approach for MRI Image De-noising and Resolution Enhancement
A Novel Approach for MRI Image De-noising and Resolution Enhancement 1 Pravin P. Shetti, 2 Prof. A. P. Patil 1 PG Student, 2 Assistant Professor Department of Electronics Engineering, Dr. J. J. Magdum
More informationNOVEL APPROACH FOR FINDING PITCH MARKERS IN SPEECH SIGNAL USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION
International Journal of Advance Research In Science And Engineering http://www.ijarse.com NOVEL APPROACH FOR FINDING PITCH MARKERS IN SPEECH SIGNAL USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION ABSTRACT
More informationClassifying the Brain's Motor Activity via Deep Learning
Final Report Classifying the Brain's Motor Activity via Deep Learning Tania Morimoto & Sean Sketch Motivation Over 50 million Americans suffer from mobility or dexterity impairments. Over the past few
More informationSINOLA: A New Analysis/Synthesis Method using Spectrum Peak Shape Distortion, Phase and Reassigned Spectrum
SINOLA: A New Analysis/Synthesis Method using Spectrum Peak Shape Distortion, Phase Reassigned Spectrum Geoffroy Peeters, Xavier Rodet Ircam - Centre Georges-Pompidou Analysis/Synthesis Team, 1, pl. Igor
More informationLong Range Acoustic Classification
Approved for public release; distribution is unlimited. Long Range Acoustic Classification Authors: Ned B. Thammakhoune, Stephen W. Lang Sanders a Lockheed Martin Company P. O. Box 868 Nashua, New Hampshire
More informationAnalysis and Design of Autonomous Microwave Circuits
Analysis and Design of Autonomous Microwave Circuits ALMUDENA SUAREZ IEEE PRESS WILEY A JOHN WILEY & SONS, INC., PUBLICATION Contents Preface xiii 1 Oscillator Dynamics 1 1.1 Introduction 1 1.2 Operational
More informationTheory of Telecommunications Networks
Theory of Telecommunications Networks Anton Čižmár Ján Papaj Department of electronics and multimedia telecommunications CONTENTS Preface... 5 1 Introduction... 6 1.1 Mathematical models for communication
More informationSeismic application of quality factor estimation using the peak frequency method and sparse time-frequency transforms
Seismic application of quality factor estimation using the peak frequency method and sparse time-frequency transforms Jean Baptiste Tary 1, Mirko van der Baan 1, and Roberto Henry Herrera 1 1 Department
More informationThe characteristic identification of disc brake squeal based on ensemble empirical mode decomposition
The characteristic identification of disc brake squeal based on ensemble empirical mode decomposition Yao LIANG 1 ; Hiroshi YAMAURA 2 1 Tokyo Institute of Technology, Japan 2 Tokyo Institute of Technology,
More informationELECTROMYOGRAPHY UNIT-4
ELECTROMYOGRAPHY UNIT-4 INTRODUCTION EMG is the study of muscle electrical signals. EMG is sometimes referred to as myoelectric activity. Muscle tissue conducts electrical potentials similar to the way
More informationImage analysis. CS/CME/BioE/Biophys/BMI 279 Oct. 31 and Nov. 2, 2017 Ron Dror
Image analysis CS/CME/BioE/Biophys/BMI 279 Oct. 31 and Nov. 2, 2017 Ron Dror 1 Outline Images in molecular and cellular biology Reducing image noise Mean and Gaussian filters Frequency domain interpretation
More informationEECS 216 Winter 2008 Lab 2: FM Detector Part I: Intro & Pre-lab Assignment
EECS 216 Winter 2008 Lab 2: Part I: Intro & Pre-lab Assignment c Kim Winick 2008 1 Introduction In the first few weeks of EECS 216, you learned how to determine the response of an LTI system by convolving
More informationQuantification of glottal and voiced speech harmonicsto-noise ratios using cepstral-based estimation
Quantification of glottal and voiced speech harmonicsto-noise ratios using cepstral-based estimation Peter J. Murphy and Olatunji O. Akande, Department of Electronic and Computer Engineering University
More informationDetection, localization, and classification of power quality disturbances using discrete wavelet transform technique
From the SelectedWorks of Tarek Ibrahim ElShennawy 2003 Detection, localization, and classification of power quality disturbances using discrete wavelet transform technique Tarek Ibrahim ElShennawy, Dr.
More informationRemoval of ocular artifacts from EEG signals using adaptive threshold PCA and Wavelet transforms
Available online at www.interscience.in Removal of ocular artifacts from s using adaptive threshold PCA and Wavelet transforms P. Ashok Babu 1, K.V.S.V.R.Prasad 2 1 Narsimha Reddy Engineering College,
More informationLab 8. Signal Analysis Using Matlab Simulink
E E 2 7 5 Lab June 30, 2006 Lab 8. Signal Analysis Using Matlab Simulink Introduction The Matlab Simulink software allows you to model digital signals, examine power spectra of digital signals, represent
More informationALTERNATIVE METHODS OF SEASONAL ADJUSTMENT
ALTERNATIVE METHODS OF SEASONAL ADJUSTMENT by D.S.G. Pollock and Emi Mise (University of Leicester) We examine two alternative methods of seasonal adjustment, which operate, respectively, in the time domain
More informationWIND SPEED ESTIMATION AND WIND-INDUCED NOISE REDUCTION USING A 2-CHANNEL SMALL MICROPHONE ARRAY
INTER-NOISE 216 WIND SPEED ESTIMATION AND WIND-INDUCED NOISE REDUCTION USING A 2-CHANNEL SMALL MICROPHONE ARRAY Shumpei SAKAI 1 ; Tetsuro MURAKAMI 2 ; Naoto SAKATA 3 ; Hirohumi NAKAJIMA 4 ; Kazuhiro NAKADAI
More informationGear Transmission Error Measurements based on the Phase Demodulation
Gear Transmission Error Measurements based on the Phase Demodulation JIRI TUMA Abstract. The paper deals with a simple gear set transmission error (TE) measurements at gearbox operational conditions that
More informationMeasurement of RMS values of non-coherently sampled signals. Martin Novotny 1, Milos Sedlacek 2
Measurement of values of non-coherently sampled signals Martin ovotny, Milos Sedlacek, Czech Technical University in Prague, Faculty of Electrical Engineering, Dept. of Measurement Technicka, CZ-667 Prague,
More informationST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform
MATEC Web of Conferences 22, 0103 9 ( 2015) DOI: 10.1051/ matecconf/ 20152201039 C Owned by the authors, published by EDP Sciences, 2015 ST Segment Extraction from Exercise ECG Signal Based on EMD and
More informationInternational Journal of Digital Application & Contemporary research Website: (Volume 1, Issue 7, February 2013)
Performance Analysis of OFDM under DWT, DCT based Image Processing Anshul Soni soni.anshulec14@gmail.com Ashok Chandra Tiwari Abstract In this paper, the performance of conventional discrete cosine transform
More informationStudy and Analysis of Various Window Techniques Used in Removal of High Frequency Noise Associated in Electroencephalogram (EEG)
Study and Analysis of Various Window Techniques Used in Removal of High Frequency Noise Associated in Electroencephalogram (EEG) Ankita Tiwari*, Rajinder Tiwari Department of Electronics and Communication
More informationThe Discrete Fourier Transform. Claudia Feregrino-Uribe, Alicia Morales-Reyes Original material: Dr. René Cumplido
The Discrete Fourier Transform Claudia Feregrino-Uribe, Alicia Morales-Reyes Original material: Dr. René Cumplido CCC-INAOE Autumn 2015 The Discrete Fourier Transform Fourier analysis is a family of mathematical
More informationDSRC using OFDM for roadside-vehicle communication systems
DSRC using OFDM for roadside-vehicle communication systems Akihiro Kamemura, Takashi Maehata SUMITOMO ELECTRIC INDUSTRIES, LTD. Phone: +81 6 6466 5644, Fax: +81 6 6462 4586 e-mail:kamemura@rrad.sei.co.jp,
More informationSpatial coherency of earthquake-induced ground accelerations recorded by 100-Station of Istanbul Rapid Response Network
Spatial coherency of -induced ground accelerations recorded by 100-Station of Istanbul Rapid Response Network Ebru Harmandar, Eser Cakti, Mustafa Erdik Kandilli Observatory and Earthquake Research Institute,
More informationRail Structure Analysis by Empirical Mode Decomposition and Hilbert Huang Transform
Tamkang Journal of Science and Engineering, Vol. 13, No. 3, pp. 267 279 (2010) 267 Rail Structure Analysis by Empirical Mode Decomposition and Hilbert Huang Transform Huan-Hsuan Ho 1 *, Po-Lin Chen 2,
More informationUser-friendly Matlab tool for easy ADC testing
User-friendly Matlab tool for easy ADC testing Tamás Virosztek, István Kollár Budapest University of Technology and Economics, Department of Measurement and Information Systems Budapest, Hungary, H-1521,
More information