The Experts below are selected from a list of 18636 Experts worldwide ranked by ideXlab platform
Tong Qin-ye - One of the best experts on this subject based on the ideXlab platform.
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Sleep stage classification based on EEG Hilbert-Huang Transform
2009 4th IEEE Conference on Industrial Electronics and Applications, 2009Co-Authors: Fan Yingle, Tong Qin-yeAbstract:The aim of this work is to propose an automatic sleep stage classification technique of electroencephalogram's signals(EEG) using Hilbert-Huang Transform. EEG signals are analyzed with the Hilbert-Huang Transform, instantaneous frequency with the physical meaning is obtained; The energy-frequency distribution of EEG was used as features parameters for each sleep stage; Ultimately using nearest neighbor method for pattern classification complete classifying sleep stage. According to experimental results of 560 samples of sleep EEG, average accuracy rate of the method achieved 81.7%. In a word, The EEG Hilbert-Huang Transform based method can be used as an effective sleep staging classification.
Fan Yingle - One of the best experts on this subject based on the ideXlab platform.
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Sleep stage classification based on EEG Hilbert-Huang Transform
2009 4th IEEE Conference on Industrial Electronics and Applications, 2009Co-Authors: Fan Yingle, Tong Qin-yeAbstract:The aim of this work is to propose an automatic sleep stage classification technique of electroencephalogram's signals(EEG) using Hilbert-Huang Transform. EEG signals are analyzed with the Hilbert-Huang Transform, instantaneous frequency with the physical meaning is obtained; The energy-frequency distribution of EEG was used as features parameters for each sleep stage; Ultimately using nearest neighbor method for pattern classification complete classifying sleep stage. According to experimental results of 560 samples of sleep EEG, average accuracy rate of the method achieved 81.7%. In a word, The EEG Hilbert-Huang Transform based method can be used as an effective sleep staging classification.
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Research of Mental EEG Classification Based on Hilbert-Huang Transform
Chinese Journal of Electron Devices, 2009Co-Authors: Fan YingleAbstract:This paper studies the classification of EEG signals during mental tasks based on Hilbert-Huang Transform method.The mental EEG signals was preprocessed by Hilbert-Huang Transform in the time-frequency field.With empirical mode decomposition(EMD),the data set was decomposed into a finite and often small number of intrinsic mode functions(IMF).Using Hilbert Transform to those IMF components yielded instantaneous amplitude and frequency.After obtaining features parameters of different mental tasks by using the amplitude standard deviation of time-frequency window,the pattern recognition method of K-neighbors was applied to optimal classification.The experiment picked three classes of mental EEG signals from the database of Colorado State University EEG research center.The mean classification accuracy rate was up to 82.54%.The features of mental EEG got by Hilbert-Huang Transform can effectively do automatic mental tasks classification.
Cai Ping - One of the best experts on this subject based on the ideXlab platform.
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Research on Hilbert-Huang Transform of Underwater Acoustic Vector Signal
Computer Simulation, 2008Co-Authors: Cai PingAbstract:An application of Hilbert-Huang Transform in underwater acoustic vector signal processing was introduced and a new method of Vector Hilbert-Huang Transform(Vector HHT)was developed.The new method first decomposes acoustic vector signal by Empirical Mode Decomposition(EMD).Then it processes each Intrinsic Mode Function(IMF) using Hilbert Transform and Vector Signal Processing.According to theoretical deduction and computer simulation,the Vector HHT heritance of advantages of Vector Signal Processing and HHT has higher gain than traditional methods.
Ömer Nezih Gerek - One of the best experts on this subject based on the ideXlab platform.
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Hilbert–Huang Transform Based Approach for Measurement of Voltage Flicker Magnitude and Frequency
Electric Power Components and Systems, 2014Co-Authors: Yasemin Onal, Dogan Gokhan Ece, Ömer Nezih GerekAbstract:AbstractVoltage flicker is a non-stationary waveform for which direct spectral analysis is not appropriate. To overcome this difficulty, a Hilbert–Huang Transform based technique is proposed here. Hilbert–Huang Transform is a new signal processing method that can be used in the analysis of non-linear and non-stationary signals. In the suggested method, the recorded voltage signal is decomposed into Hilbert–Huang Transform components, namely the empirical mode decomposition and intrinsic mode function components. These components are used in the calculation of the frequency and amplitude of voltage flicker. The clear success of empirical mode decomposition in depicting envelope variations of a sinusoidal waveform has been the main motivation for the adoption of Hilbert–Huang Transform in flicker analysis. Simulations are performed over waveforms, including single- and multiple-flicker frequencies and flicker with harmonic, voltage sag, and voltage swell. The waveforms are selected as pure sinusoids, as wel...
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Analysis of voltage flicker using Hilbert-Huang Transform
2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU), 2011Co-Authors: Yasemin Onal, Dogan Gokhan Ece, Ömer Nezih GerekAbstract:Hilbert Huang Transform (HHT), which was proposed by Huang and developed by Flandrin and his group, is a new signal processing method that can be used in the analysis of nonlinear and nonstationary signals. This study suggests an approach of using Hilbert Huang Transform to measure the voltage flicker in power systems. In the suggested method, voltage signal is decomposed into Emprical Mode Decomposition EMD and Intrinsic Mode Function IMF components. These components are used in the calculation of the frequency and amplitude of voltage flicker. The clear success of EMD in depicting envelope variations of a sinusoidal waveform has been the main motivation for the adoption of HHT in flicker analysis. Simulations are done by using input signal modulated with single flicker frequencies and multi flicker frequencies and input signals which include harmonic. Simulations show that the method may be used in voltage flicker analyse and gives good results.
William E. Eichinger - One of the best experts on this subject based on the ideXlab platform.
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Analysis of Sunspot Variability Using the Hilbert – Huang Transform
Solar Physics, 2011Co-Authors: Bradley L. Barnhart, William E. EichingerAbstract:The Hilbert – Huang Transform is a relatively new data analysis technique, which is able to analyze the cyclic components of a potentially nonlinear and nonstationary data series. Monthly sunspot number data from 1749 to 2010 were analyzed using this technique, which revealed the different variability inherent in the data including the 11-year (Schwabe), 20 – 50-year (quasi-Hale) and 60 – 120-year (Gleissberg) cycles. The results were compared with traditional Fourier analysis. The Hilbert – Huang Transform is able to provide a local and adaptive description of the intrinsic cyclic components of sunspot number data, which are nonstationary and which are the result of nonlinear processes.