Power Spectrum Estimation of the Welch Method Based on Imagery EEG

2013 ◽  
Vol 278-280 ◽  
pp. 1260-1264 ◽  
Author(s):  
Li Zhao ◽  
Yang He
Keyword(s):  
Experimental Data ◽  
Feature Extraction ◽  
Power Spectrum ◽  
Motor Imagery ◽  
Spectrum Estimation ◽  
Power Spectrum Estimation ◽  
Eeg Power Spectrum ◽  
Window Functions ◽  
Welch Method ◽  
Eeg Power

Welch method is a direct evolution from the periodogram method. Periodogram method is also commonly used in the power spectrum estimation, there are some inherent shortcomings in periodogram method, such as the variance and resolution of the spectrum estimation is not good, it does not satisfy the consistency estimation conditions and so on, so this paper uses improved periodogram method (welch method) to estimate the motor imagery EEG power spectrum, with Matlab for tools, through the simulation on the experimental data, comparativing and analysising the welch method’s different spectrum estimation properties with different window functions, which provides the theoretical guidance for selecting a suitable window function and makes the welch method play a good role in the EEG feature extraction.

The Power Spectrum Estimation of the AR Model Based on Motor Imagery EEG

2013 ◽  
Vol 706-708 ◽  
pp. 1923-1927 ◽  
Author(s):  
Li Zhao ◽  
Yang He
Keyword(s):  
Power Spectrum ◽  
Motor Imagery ◽  
Ar Model ◽  
Spectrum Estimation ◽  
Power Spectrum Estimation ◽  
Estimation Algorithms ◽  
Model Based

This paper uses three common AR model power spectrum estimation algorithms which are the Yule-Walker method, the burg method and the improved covariance method. Taking Matlab as a tool, the corresponding algorithms are used to carry out the power spectrum estimation of motor imagery EEG, the relationships and distinctions between the spectrum charts are compared in order to find the relatively appropriate algorithm for analyzing the EEG, which aims at providing a theoretical guidance for processing the motor imagery EEG and laying a foundation for further research.

The Simulation Research of Classic Spectrum Estimation Periodogram Method Based on Matlab

2014 ◽  
Vol 926-930 ◽  
pp. 2857-2860 ◽  
Author(s):  
Bo Ni Su ◽  
Hong Xie ◽  
Xi Yao Hua
Keyword(s):  
Signal Processing ◽  
Power Spectrum ◽  
Signal Detection ◽  
Spectral Resolution ◽  
Spectral Estimation ◽  
Estimation Method ◽  
Spectrum Estimation ◽  
Simulation Research ◽  
Power Spectrum Estimation ◽  
Welch Method

The spectrum estimation is a main content of modern signal processing, it is very important for random signal detection and analysis. This paper researched the classical spectrum estimation method, simulated the periodogram method, Barlett method, and Welch method of power spectrum estimation, then mainly discussed the spectral resolution about different length of data, also discussed the resolution of the spectral estimation and the performance of variance in the different way.

scholarly journals Windowing Design and Performance Assessment for Mitigation of Spectrum Leakage

2019 ◽  
Vol 94 ◽  
pp. 03001
Author(s):  
Dah-Jing Jwo ◽  
I-Hua Wu ◽  
Yi Chang
Keyword(s):  
Power Spectrum ◽  
Performance Assessment ◽  
Input Signal ◽  
Spectrum Estimation ◽  
Good Resolution ◽  
Spectral Leakage ◽  
Window Functions ◽  
Welch Method ◽  
And Performance ◽  
Fourier Transform Spectrum

This paper investigates the windowing design and performance assessment for mitigation of spectral leakage. A pretreatment method to reduce the spectral leakage is developed. In addition to selecting appropriate window functions, the Welch method is introduced. Windowing is implemented by multiplying the input signal with a windowing function. The periodogram technique based on Welch method is capable of providing good resolution if data length samples are selected optimally. Windowing amplitude modulates the input signal so that the spectral leakage is evened out. Thus, windowing reduces the amplitude of the samples at the beginning and end of the window, altering leakage. The influence of various window functions on the Fourier transform spectrum of the signals was discussed, and the characteristics and functions of various window functions were explained. In addition, we compared the differences in the influence of different data lengths on spectral resolution and noise levels caused by the traditional power spectrum estimation and various window-function-based Welch power spectrum estimations.

scholarly journals Simulated predictions for H i at z = 3.35 with the Ooty Wide Field Array (OWFA) – II. Foreground avoidance

2020 ◽  
Vol 500 (4) ◽  
pp. 4398-4413
Author(s):  
Suman Chatterjee ◽  
Somnath Bharadwaj ◽  
Visweshwar Ram Marthi
Keyword(s):  
Power Spectrum ◽  
Broad Class ◽  
Window Function ◽  
Wide Field ◽  
Spectrum Estimation ◽  
Power Spectrum Estimation ◽  
Intensity Mapping ◽  
Window Functions ◽  
Frequency Window ◽  
Optimal Window

ABSTRACT Considering the upcoming OWFA, we use simulations of the foregrounds and the z = 3.35 H i 21-cm intensity mapping signal to identify the (k⊥, k∥) modes where the expected 21-cm power spectrum P(k⊥, k∥) is substantially larger than the predicted foreground contribution. Only these uncontaminated k modes are used for measuring P(k⊥, k∥) in the “Foreground Avoidance” technique. Though the foregrounds are largely localized within a wedge. we find that the small leakage beyond the wedge surpasses the 21-cm signal across a significant part of the (k⊥, k∥) plane. The extent of foreground leakage is extremely sensitive to the frequency window function used to estimate P(k⊥, k∥). It is possible to reduce the leakage by making the window function narrower; however, this comes at the expense of losing a larger fraction of the 21-cm signal. It is necessary to balance these competing effects to identify an optimal window function. Considering a broad class of cosine window functions, we identify a six term window function as optimal for 21-cm power spectrum estimation with OWFA. Considering only the k modes where the expected 21-cm power spectrum exceeds the predicted foregrounds by a factor of 100 or larger, a $5\, \sigma$ detection of the binned power spectrum is possible in the k-ranges $0.18 \le k \le 0.3 \, {\rm Mpc}^{-1}$ and $0.18 \le k \le 0.8 \, {\rm Mpc}^{-1}$ with 1000–2000  and 104 h of observation, respectively.

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