Feature Extraction in the Time Domain: Application to the Analysis of Financial Data and Strategies over Time

1977 ◽  
pp. 75-90
Author(s):  
L. F. Pau
Keyword(s):  
Feature Extraction ◽  
Time Domain ◽  
Financial Data ◽  
The Time Domain ◽  
Over Time

scholarly journals Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

2019 ◽  
Vol 9 (23) ◽  
pp. 4990 ◽  
Author(s):  
Jusas ◽  
Samuvel
Keyword(s):  
Feature Extraction ◽  
Time Domain ◽  
Extraction Process ◽  
Computer Interface ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Fisher Ratio ◽  
Frequency Domains ◽  
Subject Specific ◽  
The Time Domain

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher′s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher′s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

scholarly journals Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Zhiqiang Peng ◽  
Yue Zhang
Keyword(s):  
Feature Extraction ◽  
Angular Velocity ◽  
Frequency Domain ◽  
Time Domain ◽  
Inertial Sensors ◽  
Extraction Methods ◽  
Feature Extraction Method ◽  
The Difference ◽  
The Time Domain ◽  
Almost All

Correctly identifying human activities is very significant in modern life. Almost all feature extraction methods are based directly on acceleration and angular velocity. However, we found that some activities have no difference in acceleration and angular velocity. Therefore, we believe that for these activities, any feature extraction method based on acceleration and angular velocity is difficult to achieve good results. After analyzing the difference of these indistinguishable movements, we propose several new features to improve accuracy of recognition. We compare the traditional features and our custom features. In addition, we examined whether the time-domain features and frequency-domain features based on acceleration and angular velocity are different. The results show that (1) our custom features significantly improve the precision of the activities that have no difference in acceleration and angular velocity; and (2) the combination of time-domain features and frequency-domain features does not significantly improve the recognition of different activities.

Research on Feature Extraction of Acoustic Emission Signals in Time-Domain

2013 ◽  
Vol 819 ◽  
pp. 171-175 ◽  
Author(s):  
Wei Wang ◽  
Qiang Li
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Time Domain ◽  
Industrial Environment ◽  
Bearing Fault ◽  
Partial Analysis ◽  
The Time Domain

Acoustic emission detecting has been widely used in the diagnosis of bearing fault, but nearly all of these implements require that the transducer placed close to the source of acoustic emission. However, in actual industrial environment, the transducer couldnt be mounted very close to the bearings. In this paper, the time-domain wave and time-domain features based methods were analyzed and compared among four channels at different rotating speeds. And partial analysis and some conclusions drawn from the analysis were listed below.

EEG-Based Motor Imagery Feature Extraction

2013 ◽  
Vol 846-847 ◽  
pp. 944-947
Author(s):  
Yang Liu ◽  
Nian Qiang Li ◽  
Yong Xiang Li
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Time Domain ◽  
Classification Accuracy ◽  
Power Spectrum Density ◽  
Continuous Wavelet ◽  
Spectrum Density ◽  
Energy Feature ◽  
The Time Domain ◽  
Selection Of

In this study, we proposed a simple and effective approach for feature extraction of motor imagery (MI) data. Aside from the original use of continuous wavelet transform (CWT), the Blackman filter is proposed to further refine the selection of active segments. In the time domain we compute the energy feature by squared-amplitude of EEG; in the frequency domain BT method power spectrum density (PSD) is used to get energy feature. The method is simple and the classification accuracy is satisfactory, especially for classification 2.

scholarly journals ELAN Analysis Companion (EAC): A Software Tool for Time-course Analysis of ELAN-annotated Data

2016 ◽  
Vol 9 (3) ◽  
Author(s):  
Richard Andersson ◽  
Olof Sandgren
Keyword(s):  
Time Domain ◽  
Time Course ◽  
Data Extraction ◽  
Software Tool ◽  
Analysis Data ◽  
Visual World ◽  
Visual World Paradigm ◽  
Free Data ◽  
The Time Domain ◽  
Over Time

LAN is a widely used and free (in both senses) annotation software for behavioral or other events that unfold over time. We report on and release a stand-alone program that expands on ELAN's capabilities in two ways: 1) it allows the researcher to plot and export time-course analysis data directly from ELAN's native annotation files, allowing for hassle-free data extraction in the time domain, e.g. for visual-world paradigm studies; and 2) it allows the researcher to weight ELAN's built-in annotator reliability rating based on the duration of the coded events. This software is released under an open license.

Apodisation in the time domain

1992 ◽  
Vol 2 (4) ◽  
pp. 615-620
Author(s):  
G. W. Series
Keyword(s):  
Time Domain ◽  
The Time Domain

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

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