Power spectrum of electroencephalogram: Mistakes and Practical Application

2021 ◽  
pp. 45-52
Author(s):  
L. B. Ivanov
Keyword(s):  
Spectral Analysis ◽  
Power Spectrum ◽  
Spatial Organization ◽  
Alpha Rhythm ◽  
Computer Systems ◽  
Data Interpretation ◽  
Practical Application ◽  
Eeg Spectral Analysis ◽  
Analysis Technique ◽  
Human Psyche

In this lecture, the state of the practical use of spectral analysis of EEG is analyzed in detail. The author focuses on the reasons for the slow introduction of spectral analysis into everyday practice and identifies ways to overcome this deficiency. The reason for the need to master this useful analysis technique has been substantiated. The algorithm for performing spectral analysis of EEG in practice, both at the stage of processing and at the stage of data interpretation, is presented in detail. In the lecture, the author outlined the features of the options for the spatial organization of the alpha rhythm and their relationship with the characteristics of the human psyche. One of the reasons that hinders the use of EEG spectral analysis in practice is the lag of the software of electroencephalographic computer systems of leading manufacturers from modern requirements of the time.

EEG spectral analysis after minor head injury in man

1988 ◽  
Vol 70 (2) ◽  
pp. 185-189 ◽  
Author(s):  
M.T. Tebano ◽  
M. Cameroni ◽  
G. Gallozzi ◽  
A. Loizzo ◽  
G. Palazzino ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Head Injury ◽  
Minor Head Injury ◽  
Eeg Spectral Analysis

scholarly journals An Improved Method for Discerning Broken Rotor Bar Fault and Load Oscillation in Induction Motors

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3130 ◽  
Author(s):  
Liling Sun ◽  
Boqiang Xu
Keyword(s):  
Spectral Analysis ◽  
Theoretical Basis ◽  
Induction Motors ◽  
The Other ◽  
Improved Method ◽  
Broken Rotor Bar ◽  
Signal Parameters ◽  
Analysis Technique ◽  
Novel Strategy ◽  
The One

A few methods for discerning broken rotor bar (BRB) fault and load oscillation in induction motors have been reported in the literature. However, they all perhaps inevitably fail in adverse cases in which these two phenomena are simultaneously present. To tackle this problem, an improved method for discerning BRB fault and load oscillation is proposed in this paper based on the following work. On the one hand, the theoretical basis is analytically extended to include such an adverse case, yielding some important findings on the spectra of the instantaneous reactive and active powers. A novel strategy is thus outlined to correctly discern BRB fault and load oscillation even when simultaneously present. On the other hand, Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) is adopted as the spectral analysis technique to deal with the instantaneous reactive and active powers, yielding a certain improvement compared to the existing methods, adopting Fast Fourier Transform (FFT). Simulation and experimental results demonstrate that the proposed method can correctly discern BRB fault and load oscillation even when simultaneously present.

A Hybrid Technique Using PCA and Wavelets in Network Traffic Anomaly Detection

2014 ◽  
Vol 6 (1) ◽  
pp. 17-53 ◽  
Author(s):  
Stevan Novakov ◽  
Chung-Horng Lung ◽  
Ioannis Lambadaris ◽  
Nabil Seddigh
Keyword(s):  
Spectral Analysis ◽  
Statistical Analysis ◽  
Wavelet Analysis ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Hybrid Approach ◽  
Haar Wavelet ◽  
Wavelet Filtering ◽  
Analysis Technique ◽  
Network Anomaly Detection

Research into network anomaly detection has become crucial as a result of a significant increase in the number of computer attacks. Many approaches in network anomaly detection have been reported in the literature, but data or solutions typically are not freely available. Recently, a labeled network traffic flow dataset, Kyoto2006+, has been created and is publicly available. Most existing approaches using Kyoto2006+ for network anomaly detection apply various clustering techniques. This paper leverages existing well known statistical analysis and spectral analysis techniques for network anomaly detection. The first popular approach is a statistical analysis technique called Principal Component Analysis (PCA). PCA describes data in a new dimension to unlock otherwise hidden characteristics. The other well known spectral analysis technique is Haar Wavelet filtering analysis. It measures the amount and magnitude of abrupt changes in data. Both approaches have strengths and limitations. In response, this paper proposes a Hybrid PCA–Haar Wavelet Analysis. The hybrid approach first applies PCA to describe the data and then Haar Wavelet filtering for analysis. Based on prototyping and measurement, an investigation of the Hybrid PCA–Haar Wavelet Analysis technique is performed using the Kyoto2006+ dataset. The authors consider a number of parameters and present experimental results to demonstrate the effectiveness of the hybrid approach as compared to the two algorithms individually.

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