scholarly journals Research on High-Frequency Combination Coding-Based SSVEP-BCIs and Its Signal Processing Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Feng Zhang ◽  
Chengcheng Han ◽  
Lili Li ◽  
Xin Zhang ◽  
Jun Xie ◽  
...  
Keyword(s):  
High Frequency ◽  
Recognition Rate ◽  
Epileptic Seizures ◽  
New Paradigm ◽  
Safety Hazards ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
High Frequency Response ◽  
Signal Processing Algorithms ◽  
The Stability

This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The new paradigm is High-Frequency Combination Coding-Based SSVEP (HFCC-SSVEP). The goal of this study is to increase the number of targets using fewer stimulation frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. This paper investigated the HFCC-SSVEP high-frequency response (beyond 25 Hz) for 3 frequencies (25 Hz, 33.33 Hz, and 40 Hz). HFCC-SSVEP producesnnwithnhigh stimulation frequencies through Time Series Combination Code. Furthermore, The Improved Hilbert-Huang Transform (IHHT) is adopted to extract time-frequency feature of the proposed SSVEP response. Lastly, the differentiation combination (DC) method is proposed to select the combination coding sequence in order to increase the recognition rate; as a result, IHHT algorithm and DC method for the proposed SSVEP paradigm in this study increase recognition efficiency so as to improve ITR and increase the stability of the BCI system. Furthermore, SSVEPs evoked by high-frequency stimuli (beyond 25 Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures. This study tests five subjects in order to verify the feasibility of the proposed method.

Applied Research on Segmented-Hilbert-Huang Transform Algorithm in Fault Feature Extraction of Main Reducer

2013 ◽  
Vol 823 ◽  
pp. 417-421 ◽  
Author(s):  
Feng Yun Huang ◽  
Huan Huan Sun ◽  
Hao Pan ◽  
Wei Ru Zhang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Vibration Signal ◽  
Model Decomposition ◽  
Frequency Dictionary ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
Scale Characteristics ◽  
Frequency Impulse

For the multi-time scale characteristics of vibration signal, a composite multi-frequency dictionary combining the low-frequency Fourier dictionary and the high-frequency impulse time-frequency dictionary is constituted, to decompose multi-component vibration signal into the combination of several one-component signals. The use of empirical model decomposition (EDM) in high-frequency impulse Component signal including feature information is to realize segmented Hilbert-Huang transform of signal and to acquire the time-frequency representation of every one-component signal, which is the process of fault information extraction of vibration signal. The application of the method in main reducer fault diagnosis verifies the engineering practicability and validity of the new algorithm.

scholarly journals Hilbert-Huang Transform Analysis of Machining Stability in Ball-Nose End-Milling of Curved Surface

2020 ◽  
Vol 14 (3) ◽  
pp. 500-511
Author(s):  
Muizuddin Azka ◽  
Keiji Yamada ◽  
Mahfudz Al Huda ◽  
Kyosuke Mani ◽  
Ryutaro Tanaka ◽  
...  
Keyword(s):  
Cutting Force ◽  
Inclination Angle ◽  
End Milling ◽  
Curved Surface ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Machining Errors ◽  
Cutting Velocity ◽  
Cylindrical Workpiece ◽  
The Stability

This paper investigates the machining stability in ball-end-milling of curved surface in which the inclination of tool continuously changes. Initially, the influence of inclination angle is geometrically investigated on the parameters such as immersion angle and cutting velocity. Then, the paper presents the stability lobe diagrams of the process. Curved surface milling is simulated by slot milling on a cylindrical workpiece using a ball-end-mill to obtain the cutting force and vibration, which are used for fast-Fourier transform and Hilbert-Huang transform (HHT) analyses. Experimental results show that the cutting force increases, and the stability becomes worse with the inclination angle, while the machining errors decrease with the inclination. The vibration analysis showed that the HHT can detect the transition from stable to unstable during milling of curved surface in the time-frequency plots.

Analysis of Motor Imagery EEG Based on Hilbert-Huang Transform

2014 ◽  
Vol 998-999 ◽  
pp. 833-837
Author(s):  
Xiao Lin Zhu ◽  
Jian Ping Liu ◽  
Xiao Nan Zhang
Keyword(s):  
Motor Imagery ◽  
Recognition Rate ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Left Hand ◽  
Time Frequency ◽  
The Hilbert Transform ◽  
Electroencephalogram Eeg ◽  
Emd Method

Based on Hilbert-Huang Transform (HHT) theory, we present a method to analyze the electroencephalogram (EEG) signal of right and left hand motor imagery. Firstly, EMD method decomposed EEG signal into a group of intrinsic mode functions (IMFs). The first three IMFs were extracted to denoise. We adopt endpoint Mirror Extension method to relieve the influence on subsequent processing brought by endpoint effect. According to the Hilbert transform, we can obtain the time-frequency distribution. The energy of the first three components is selected as the input of SVM. The results show that EMD is an efficient method to analyze the EEG signal. The proposed method obtains an ideal recognition rate.

scholarly journals Dynamic deformation monitoring of an offshore platform structure with accelerometers

2021 ◽  
Author(s):  
Wen Li ◽  
Craig Hancock ◽  
Yusong Yang ◽  
Jian Wang ◽  
Xiaolin Meng
Keyword(s):  
High Frequency ◽  
Structural Characteristics ◽  
Three Dimensional ◽  
Deformation Monitoring ◽  
Offshore Platforms ◽  
Dynamic Displacement ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Average Value ◽  
The North

AbstractIn this paper, structural characteristics are evaluated by displacement and frequency indicators that indicate the real-time health status of offshore platforms. This paper uses an accelerometer to collect the dynamic response of the platform in the event of a ship collision. The main contributions of this research are reflected in three aspects. Firstly, based on Empirical Mode Decomposition (EMD) multiscale decomposition, the noise range is determined according to the scale and the average value of the standardized accumulation mode, and the original acceleration sequence is denoised. Secondly, two impact tests were carried out to understand the platform's structural characteristics under an external load. Combined with the FFT algorithm and Hilbert Huang transform, the three-dimensional information of the time, frequency, and energy is analyzed. Finally, a method of high-frequency dynamic displacement reconstruction is proposed. According to the extracted vibration frequency information, the parameters for the filter are reasonably set, and the denoised acceleration time sequence is processed with bandpass filtering and quadratic integration to obtain the high-frequency dynamic displacement of the structure. The results show that the high-frequency dynamic displacement of the accelerometer reconstruction is 1.5 mm. Two collision event frequencies, 1.477 Hz and 1.483 Hz, were successfully extracted from the north direction.

scholarly journals Electrophysiological aftereffects of high-frequency transcranial random noise stimulation (hf-tRNS): an EEG investigation

2021 ◽  
Author(s):  
Filippo Ghin ◽  
Louise O’Hare ◽  
Andrea Pavan
Keyword(s):  
High Frequency ◽  
Discrimination Task ◽  
Temporal Dynamics ◽  
Random Noise ◽  
Decomposition Analysis ◽  
Oscillatory Activity ◽  
Motion Direction ◽  
Time Frequency ◽  
Direction Discrimination ◽  
Transcranial Random Noise Stimulation

AbstractThere is evidence that high-frequency transcranial random noise stimulation (hf-tRNS) is effective in improving behavioural performance in several visual tasks. However, so far there has been limited research into the spatial and temporal characteristics of hf-tRNS-induced facilitatory effects. In the present study, electroencephalogram (EEG) was used to investigate the spatial and temporal dynamics of cortical activity modulated by offline hf-tRNS on performance on a motion direction discrimination task. We used EEG to measure the amplitude of motion-related VEPs over the parieto-occipital cortex, as well as oscillatory power spectral density (PSD) at rest. A time–frequency decomposition analysis was also performed to investigate the shift in event-related spectral perturbation (ERSP) in response to the motion stimuli between the pre- and post-stimulation period. The results showed that the accuracy of the motion direction discrimination task was not modulated by offline hf-tRNS. Although the motion task was able to elicit motion-dependent VEP components (P1, N2, and P2), none of them showed any significant change between pre- and post-stimulation. We also found a time-dependent increase of the PSD in alpha and beta bands regardless of the stimulation protocol. Finally, time–frequency analysis showed a modulation of ERSP power in the hf-tRNS condition for gamma activity when compared to pre-stimulation periods and Sham stimulation. Overall, these results show that offline hf-tRNS may induce moderate aftereffects in brain oscillatory activity.

Comparison of wavelet continuous transform and signal averaged ECG for high frequency content and late potential detection in healthy individuals

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Garcia Iglesias ◽  
J.M Rubin Lopez ◽  
D Perez Diez ◽  
C Moris De La Tassa ◽  
F.J De Cos Juez ◽  
...  
Keyword(s):  
High Frequency ◽  
Classical Method ◽  
Total Power ◽  
Frequency Content ◽  
Healthy Individuals ◽  
Time Frequency ◽  
Qt Intervals ◽  
Long Time ◽  
Power Content ◽  
High Frequency Content

Abstract Introduction The Signal Averaged ECG (SAECG) is a classical method forSudden Cardiac Death (SCD) risk assessment, by means of Late Potentials (LP) in the filtered QRS (fQRS)[1]. But it is highly dependent on noise and require long time records, which make it tedious to use. Wavelet Continuous Transform (WCT) meanwhile is easier to use, and may let us to measure the High Frequency Content (HFC) of the QRS and QT intervals, which also correlates with the risk of SCD [2,3]. Whether the HFC of the QRS and QT measured with the WCT is a possible subrogate of LP, has never been demonstrated. Objective To demonstrate if there is any relationship between the HFC measured with the WCT and the LP analyzed with the SAECG. Methods Data from 50 consecutive healthy individuals. The standard ECG was digitally collected for 3 consecutive minutes. For the WCT Analysis 8 consecutive QT complexes were used and for the SAECG Analysis all available QRS were used. The time-frequency data of each QT complex were collected using the WCT as previously described [3] and the Total, QRS and QT power were obtained from each patient. For the SAECG, bipolar X, Y and Z leads were used with a bidirectional filter at 40 to 250 Hz [1]. LP were defined as less than 0.05 z in the terminal part of the filtered QRS and the duration (SAECG LP duration) and root mean square (SAECG LP Content) of this LP were calculated. Pearson's test was used to correlate the Power content with WCT analysis and the LP in the SAECG. Results There is a strong correlation between Total Power and the SAECG LP content (r=0.621, p<0.001). Both ST Power (r=0.567, p<0.001) and QRS Power (r=0.404, p=0.004) are related with the SAECG LP content. No correlation were found between the Power content (Total, QRS or ST Power) and the SAECG LP duration. Also no correlation was found between de SAECG LP content and duration. Conclusions Total, QRS and ST Power measured with the WCT are good surrogates of SAECG LP content. No correlation were found between WCT analysis and the SAECG LP duration. Also no correlation was found between the SAECG LP content and duration. This can be of high interest, since WCT is an easier technique, not needing long recordings and being less affected by noise. Funding Acknowledgement Type of funding source: None

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