Hilbert-Huang Transform and Welch's Method for Motor imagery based Brain Computer Interface

This paper explores the possibility of using a combination of the empirical mode decomposition (EMD) and the Hilbert transform (HT), termed the Hilbert-Huang transform (HHT) method, to identify the modal damping ratios of the structure with closely spaced modal frequencies. The principle of the HHT method and the procedure of using the HHT method for modal damping ratio identification are briefly introduced first. The dynamic response of a two-degrees-of-freedom (2DOF) system under an impact load is then computed for a wide range of dynamic properties from well-separated modal frequencies to very closely spaced modal frequencies. The natural frequencies and modal damping ratios identified by the HHT method are compared with the theoretical values and those identified using the fast Fourier transform (FFT) method. The results show that the HHT method is superior to the FFT method in the identification of modal damping ratios of the structure with closely spaced modes of vibration. Finally, a 36-storey shear building with a 4-storey light appendage, having closely spaced modal frequencies and subjected to an ambient ground motion, is analyzed. The modal damping ratios identified by the HHT method in conjunction with the random decrement technique (RDT) are much better than those obtained by the FFT method. The HHT method performing in the frequency-time domain seems to be a promising tool for system identification of civil engineering structures.

This paper presents a motor imagery based Brain Computer Interface (BCI) that uses single channel EEG signal from the C3 or C4 electrode placed in the motor area of the head. Time frequency analysis using Short Time Fourier Transform (STFT) is used to compute spectrogram from the EEG data. The STFT is scaled to have gray level values on which Grey Co-occurrence Matrix (GLCM) is computed. Texture descriptors such as correlation, energy, contrast, homogeneity and dissimilarity are calculated from the GLCM matrices. The texture descriptors are used to train a logistic regression classifier which is then used to classify the left and right motor imagery signals. The single-channel motor imagery classification system is tested offline with different subjects. The average offline accuracy is 87.6%. An online BCI system is implemented in openViBE with the single channel classification scheme. The stimuli presentations and feedback are implemented in Python and integrated with the openViBe BCI system.

In the last few years, Brain Computer Interfaces (BCI) attempted the attention of many researchers. In Motor Imagery (MI)-BCI, central nervous system directly connected to a computer or an automation system. Characteristics of the electromyographic (EEG) signals are utilized in MI-BCI systems. Various techniques have been proposed to extract EEG signal characteristics during recent years. The main objective of this research is to employ an efficient deep learning approach to extract the features of EEG signals using composition of convolutional Neural Network and discrete wavelet transform utilized in the BCI system. The deep learning approach presented in this study has rarely been explored to employ for EEG features extraction. The simulation study indicates that the presented method carry out remarkable accuracy and high performance compared with conventional approaches such as support vector machine and artificial Neural Network methods and give a powerful indicative decision making aid to assist physicians in the treatment of the right and left-hand features for real time motor imagery classification system. Furthermore, the most advantages of employing the proposed method are to eliminate the feature selection level and reducing the processing cost significantly.

This study investigates whether the combined effect of kinesthetic motor imagery-based brain computer interface (KI-BCI) and transcranial direct current stimulation (tDCS) on upper limb function in subacute stroke patients is more effective than using KI-BCI or tDCS alone. Forty-eight subacute stroke survivors were randomized to the KI-BCI, tDCS, or BCI-tDCS group. The KI-BCI group performed 30 min of KI-BCI training. Patients in tDCS group received 30 min of tDCS. Patients in BCI-tDCS group received 15 min of tDCS and 15 min of KI-BCI. The treatment cycle was five times a week, for four weeks. After all intervention, the Fugl-Meyer Assessment-Upper Extremity, Motor Status Scale, and the Modified Barthel Index scores of the KI-BCI group were superior to those of the tDCS group. The BCI-tDCS group was superior to the tDCS group in terms of the Motor Status Scale. Although quantitative EEG showed no significant group differences, the quantitative EEG indices in the tDCS group were significantly lower than before treatment. In conclusion, after treatment, although all intervention strategies improved upper limb motor function and daily living abilities in subacute stroke patients, KI-BCI demonstrated significantly better efficacy than tDCS. Under the same total treatment duration, the combined use of tDCS and KI-BCI did not achieve the hypothesized optimal outcome. Notably, tDCS reduced QEEG indices, possibly indicating favorable future outcomes in future.Trial registry number: ChiCTR2000034730.

Conventional techniques for feature extraction are based on the stationary process and linear theory, so they are unsuited for processing the non-stationary signals. Hilbert-Huang transform (HHT) has been proposed recently and is specially fit to explore the non-stationary signals. The paper utilizes HHT to analyze the signals from the WZ12-1 platform. First, we employ a simulation signal to measure the performance of empirical mode decomposition (EMD), which is the core of HHT. Next, the HHT method is applied to extract the fault feature from the signals collected from the WZ12-1 platform and successfully reveals the reason causing the excessive vibration of the WZ12-1 platform. The results indicate that HHT could be a helpful method for the fault feature extraction of mechanical systems.Keywordsfeature extractionHilbert-Huang transform (HHT)empirical mode decomposition (EMD)offshore platform

Fabric defect detection has been an active area of research since a long time and still a robust system is needed which can fulfill industrial requirements. A robust automatic fabric defect detection system (FDDS) would results in quality products and more revenues. Many different approaches and method have been tried to implement FDDS. This paper presents a new scheme for automated FDDS implementation using Hilbert-Huang Transform (HHT) and also compares it with O penCV approach. In my implementation of both approaches in same environment, the HHT approach produces higher defect detection accuracies than OpenCV approach and more computationally efficient. The article is divided into four parts, the first part is the introduction of t wo methods, the second part for the two methods defect detection system implementation, the third pa rt is the results and the discussion of two methods, the fourth part is the conclusion. HHT is used to deal with nonlinear and non-stationary signal, the method of its adaptive is good, can be used to analyze all kinds of signals. Compared with the Fourier transform, the HHT has more explicit time- frequency description; filter performance is even more acute. In addition, its implementation is simple and can be real-time calculation. Therefore, for the engineering application and theory research, HHT has very important significance. HHT method has two parts: Empirical mode decomposition and Hilbert spectrum analysis.

This paper presents recent time-frequency analysis technique called Hilbert Huang Transform (HHT) used for non-stationary and nonlinear signals. This technique is applied in combination with a pattern recognition method to detect defects in induction motor. Studied defects in our case are one broken rotor bar, two broken rotor bars and broken ring on the rotor. The aim of this study is to improve the classification results of these different rotor faults. The classification results obtained by using the HHT method and those obtained by using the temporal signals are compared.

本文提出了基于希尔伯特–黄变换(Hilbert-Huang Transform, HHT)与Teager能量算子(Teager Energy Operator, TEO)相融合的谐振接地选线新方法。首先对零序电流信号进行经验模态分解法(Empirical Mode De-composition, EMD)分解，然后将TEO合理的引入选线算法中，并与HHT极性判别法相融合，实现了HHT与TEO两者融合的优势互补。通过仿真分析，验证了所提方法的可行性和有效性。 The combination method of Hilbert-Huang Transform (HHT) and Teager Energy Operator (TEO) for fault line selection with resonance grounding is proposed in this paper. The zero sequence current signals are decomposed using Empirical Mode Decomposition (EMD). The TEO is reasonable introduced into algorithm of fault line selection. The TEO is combined with the method of HHT polarity estimate. The complementary advantage for combination method of HHT and TEO is implemented. The feasibility and effectiveness of the proposed method is verified by simulation results.

We propose a method based on the Hilbert–Huang transform (HHT) to recover the high-energy waveform of low-frequency quasiperiodic oscillations (QPOs). Based on the method, we successfully obtain the modulation of the phase-folded light curve above 170 keV using the QPO phase reconstructed at lower energies in MAXI J1535–571 with Insight-HXMT observations. A comprehensive simulation study is conducted to demonstrate that such modulation indeed originates from the QPO. Thus, the highest energies turn out to significantly exceed the upper limit of ∼100 keV for QPOs reported previously using the Fourier method, marking the first opportunity to study QPO properties above 100 keV in this source. Detailed analyses of these high-energy QPO profiles reveal different QPO properties between the 30–100 and 100–200 keV energy ranges: the phase lag remains relatively stable, and the amplitude slightly increases below ∼100 keV, whereas above this threshold, soft phase lags and a decrease in amplitude are observed. Given the reports of a hard-tail detection in broad spectroscopy, we propose that the newly discovered QPO properties above 100 keV are dominated by the hard-tail component, possibly stemming from a relativistic jet. Our findings also indicate a strong correlation between the QPOs originating from the jet and corona, supporting the scenario of jet–corona coupling precession. We emphasize that our proposed HHT-based method can serve as an efficient manner in expanding the high-energy band for studying QPOs, thereby enhancing our understanding of their origin.

Fluctuating pressure is an important feature of the bottom of a stilling basin with step-down floor. To analyze the frequency domain characteristics and energy distribution of this fluctuating pressure, the Hilbert–Huang transform (HHT) method is used. First, empirical mode decomposition is performed on the pressure fluctuation signal to obtain a number of intrinsic mode functions (IMFs), and then the Hilbert transformation is performed on each IMF to obtain the Hilbert spectrum and marginal spectrum for characterizing the pressure fluctuation signal. The results show that the fluctuating pressure signal of the stilling basin with step-down floor has obvious characteristics of low frequency and large amplitude. The dominant frequencies of the head and tail of the stilling basin are very prominent, and most of the energy is concentrated below 5.0 Hz; with the increase in the relative position of the measuring point, the energy distribution in stilling basin with step-down floor changes from high-frequency component to low-frequency component. The fluctuating pressure signal of the stilling basin with step-down floor has random amplitude modulation and frequency modulation. The marginal spectrum obtained by the HHT method can obtain the local characteristics of the signal more accurately and is more suitable for processing nonlinear and nonstationary signals.

The Hilbert–Huang transform method is used in this work to analyze wave records from the North Sea, which include abnormal waves. The analysis of the characteristics of abnormal waves is based on the local decomposition of wave data by using the intrinsic mode functions. The variations of amplitude of intrinsic mode functions around the time of the occurrence of abnormal waves are examined in order to investigate the contribution of different intrinsic mode functions on the abnormal waves’ profile, in an attempt to find relationships between them. The changes of local frequency of each intrinsic mode function in the vicinity of abnormal waves are examined. The essential nonlinearity of abnormal waves is considered as intrawave frequency modulation, a variation of the instantaneous frequency within one oscillation cycle. The Hilbert spectrum is used as a detector of abnormal waves.

Atomic clock difference (errors) contains various kinds of noise and it can be characterized to as non-stationary. Hilbert-Huang Transform (HHT), a new adaptive method based on empirical mode decomposition (EMD) and Hilbert spectral analysis, was used to analyze and process atomic clock difference. Taking real data for discussion, the results show that HHT method is practical and versatile to handle noisy data and characterize the clock behavior.

To investigate the annual and interannual variability of ocean surface wind over the South China Sea (SCS), the vector empirical orthogonal function (VEOF) method and the Hilbert-Huang transform (HHT) method were employed to analyze a set of combined satellite scatterometer wind data during the period from December 1992 to October 2009. The merged wind data were generated from European Remote Sensing Satellite (ERS)-1/2 Scatterometer, NASA Scatterometer (NSCAT) and NASA’s Quick Scatterometer (QuikSCAT) wind products. The first VEOF mode corresponds to a winter-summer mode which accounts for 87.3% of the total variance and represents the East Asian monsoon features. The second mode of VEOF corresponds to a spring-autumn oscillation which accounts for 8.3% of the total variance. To analyze the interannual variability, the annual signal was removed from the wind data set and the VEOFs of the residuals were calculated. The temporal mode of the first interannual VEOF is correlated with the Southern Oscillation Index (SOI) with a four-month lag. The second temporal interannual VEOF mode is correlated with the SOI with no time lag. The time series of the two interannual VEOFs were decomposed using the HHT method and the results also show a correlation between the interannual variability and El Nino-Southern Oscillation (ENSO) events.

A large amount of calibration data is typically needed to train an electroencephalogram (EEG)-based brain-computer interfaces (BCI) due to the non-stationary nature of EEG data. This paper proposes a novel weighted transfer learning algorithm using a dynamic time warping (DTW) based alignment method to alleviate this need by using data from other subjects. DTW-based alignment is first applied to reduce the temporal variations between a specific subject data and the transfer learning data from other subjects. Next, similarity is measured using Kullback Leibler divergence (KL) between the DTW aligned data and the specific subject data. The other subjects' data are then weighted based on their KL similarity to each trials of the specific subject data. This weighted data from other subjects are then used to train the BCI model of the specific subject. An experiment was performed on publicly available BCI Competition IV dataset 2a. The proposed algorithm yielded an average improvement of 9% compared to a subject-specific BCI model trained with 4 trials, and the results yielded an average improvement of 10% compared to naive transfer learning. Overall, the proposed DTW-aligned KL weighted transfer learning algorithm show promise to alleviate the need of large amount of calibration data by using only a short calibration data.

To improve the spatial resolution of the electroen-cephalogram (EEG) signal, it is conventional to use a large number of scalp electrode while recording oscillatory rhythms in motor imagery based brain-computer interface(MI-BCI). However, this increases the dimension of data and might fail to generalize and thus over-fit. Therefore, it is required to reduce the dimension of input data in an optimal way. In this paper, we propose a method using an artificial neural network to reduce the dimension of EEG for MI-BCI. We train an under-complete sparse autoencoder neural network for each subject separately to encode the EEG data optimally. The optimally encoded EEG trials are then used by the Filter Bank Common Spatial Pattern (FBCSP) method to decode the imagined motor movement. In similar lines, autoencoder was trained on subject independent data. We achieved improved motor imagery classification accuracy when the dimension of the data was almost reduced by half compared to the state-of art FBCSP. The performance of the proposed method is also compared with Sparse Common Spatial Pattern (SCSP) based channel selection method. The average classification accuracy obtained for 10 subjects is 74.3±8.06 % with only 13 encoded channels. Also, for the autoencoder trained to be subject independent we obtained an average classification accuracy of 66.64±3.93% with only 11 encoded channels after crossvalidation. The study extends the use of autoencoder neural networks in motor imagery based brain-computer interface and shows significant improvement in performance with reduced data dimension.