scholarly journals ELAN: A Software Package for Analysis and Visualization of MEG, EEG, and LFP Signals

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Pierre-Emmanuel Aguera ◽  
Karim Jerbi ◽  
Anne Caclin ◽  
Olivier Bertrand
Keyword(s):  
Frequency Analysis ◽  
Software Package ◽  
Signal Analysis ◽  
Large Data ◽  
Multidimensional Analysis ◽  
Spatiotemporal Resolution ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Strong Trend ◽  
Wide Range

The recent surge in computational power has led to extensive methodological developments and advanced signal processing techniques that play a pivotal role in neuroscience. In particular, the field of brain signal analysis has witnessed a strong trend towards multidimensional analysis of large data sets, for example, single-trial time-frequency analysis of high spatiotemporal resolution recordings. Here, we describe the freely available ELAN software package which provides a wide range of signal analysis tools for electrophysiological data including scalp electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG, and local field potentials (LFPs). The ELAN toolbox is based on 25 years of methodological developments at the Brain Dynamics and Cognition Laboratory in Lyon and was used in many papers including the very first studies of time-frequency analysis of EEG data exploring evoked and induced oscillatory activities in humans. This paper provides an overview of the concepts and functionalities of ELAN, highlights its specificities, and describes its complementarity and interoperability with other toolboxes.

Applications of Time-Frequency Analysis to Structures With Internal Resonance

1995 ◽  
Author(s):  
Kyle D. Dippery ◽  
Suzanne Weaver Smith
Keyword(s):  
Structural Dynamics ◽  
Frequency Analysis ◽  
Internal Resonance ◽  
Signal Analysis ◽  
Frequency Content ◽  
Modal Interactions ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Transition To Chaos ◽  
Changes Over Time

Abstract Time-frequency analysis is an approach to characterizing the nature of signals whose frequency content changes over time. Although the primary applications of this field have, to date, been in the area of communications and signal analysis, it is becoming known in the field of structural dynamics. This paper explores the application of two straightforward time-frequency techniques to several structures that exhibit internal resonance. In particular, the systems analyzed exhibit simple modal interactions and, in one case, a transition to chaos. While other methods exist for analysis of these types of behaviors, larger systems with more complex resonances maybe better analyzed with time-frequency techniques.

Time Frequency Analysis of Dispersive Waves by Means of Wavelet Transform

1995 ◽  
Vol 62 (4) ◽  
pp. 841-846 ◽  
Author(s):  
Kikuo Kishimoto ◽  
Hirotsugu Inoue ◽  
Makoto Hamada ◽  
Toshikazu Shibuya
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Gabor Wavelet ◽  
Flexural Wave ◽  
Lateral Impact ◽  
Dispersive Waves ◽  
Time Frequency Analysis ◽  
Arrival Times ◽  
Time Frequency ◽  
Wide Range

A new approach is presented for investigating the dispersive character of structural waves. The wavelet transform is applied to the time-frequency analysis of dispersive waves. The flexural wave induced in a beam by lateral impact is considered. It is shown that the wavelet transform using the Gabor wavelet effectively decomposes the strain response into its time-frequency components. In addition, the peaks of the time-frequency distribution indicate the arrival times of waves. By utilizing this fact, the dispersion relation of the group velocity can be accurately identified for a wide range of frequencies.

A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

2011 ◽  
Vol 204-210 ◽  
pp. 973-978
Author(s):  
Qiang Guo ◽  
Ya Jun Li ◽  
Chang Hong Wang
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Low Snr ◽  
The Time Domain ◽  
Signal Analysis Method

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

Wavelet Analysis: Mother Wavelet Selection Methods

2013 ◽  
Vol 393 ◽  
pp. 953-958 ◽  
Author(s):  
Wai Keng Ngui ◽  
M. Salman Leong ◽  
Lim Meng Hee ◽  
Ahmed M. Abdelrhman
Keyword(s):  
Wavelet Analysis ◽  
Frequency Analysis ◽  
Singularity Analysis ◽  
Selection Methods ◽  
Mother Wavelet ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Main Challenge ◽  
Wide Range ◽  
Wavelet Selection

Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

Research on Joint Time-Frequency Analysis Methods of Adaptive Radar Signal

2014 ◽  
Vol 989-994 ◽  
pp. 4009-4013 ◽  
Author(s):  
Qiang Xing ◽  
Wei Gang Zhu ◽  
Yuan Bo ◽  
Kang Wang
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Radar Signal ◽  
Analysis Method ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Adaptive Radar ◽  
Short Time

Faced with complex electromagnetic environment and varieties of adaptive radar waveforms, radar signal analysis and identification becomes more and more complex. Considering two important physical quantities - time and frequency in modern signal processing methods, this paper proposes that the joint time-frequency analysis (JTFA) method based on fractional Fourier transform (FrFT) and short-time Fourier transform (STFT) is applied to adaptive radar signal processing. The simulation results show that the joint time-frequency analysis method is superior to single short-time Fourier transform, getting a better analysis of results. The joint time-frequency analysis method provides the joint distribution of the time domain and frequency domain for adaptive radar signal analysis and describes the relationship between signal frequency and time.

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