Wavelet Frequency Estimation Parameter Of Energy Distribution For Electrooculograph Signal Analysis

Jurnal Teknologi ◽

10.11113/jt.v54.816 ◽

2012 ◽

Author(s):

Wan Mohd. Bukhari Wan Daud ◽

Rubita Sudirman

Keyword(s):

Wavelet Transform ◽

Energy Distribution ◽

Eye Movement ◽

Signal Analysis ◽

Frequency Estimation ◽

Data Acquisition System ◽

Wavelet Method ◽

Distribution Features ◽

Eeg Data ◽

Recorded Data

Pengajian ini mengkaji electrooculograph (EOG) isyarat pola gerakan mata. Perilaku dari isyarat gerakan mata dijelaskan menggunakan kaedah wavelet dan digabungkan dengan ciriciri pengedaran tenaga. Ciri–ciri yang berasal dari isyarat EOG daripada empat jenis pergerakan mata dan dicatat menggunakan Sistem Akuisisi Data EEG, EEG Neurofax–9200. Elektrodelektrod tersebut diletakkan di dahi dan di bawah mata. Data diperolehi daripada 15 subjek di dalam bilik yang senyap, di mana data yang tercatat terdiri daripada empat gerakan mata yang berbeza, iaitu pergerakan ke atas, ke bawah, ke kiri dan ke kanan. Algoritma Wavelet scalogram digunakan untuk menganalisa isyarat yang direkodkan kerana ia mampu untuk menunjukkan amaun tenaga isyarat EOG pergerakan mata dengan perubahan masa dan frekuensi. Hasil kajian menunjukkan bahawa amaun tenaga isyarat EOG menunjukkan pola yang berbeza dalam gerakan–gerakan berikut: tahap 6 (8–16 Hz) untuk gerakan mata ke kiri; tahap 7 (4–8 Hz) untuk gerakan ke atas; tahap 8 (2 – 4 Hz) untuk gerakan ke kanan dan peringkat 9 (1–2 Hz) untuk gerakan ke bawah. Kata kunci: Electro–oculogram; gerakan mata; tenaga isyarat; transformasi wavelet; scalogram The study investigates the electrooculograph (EOG) signals of eye movement patterns. The behaviours of the eye movement signal is described using wavelet method and combined with the energy distribution features. The features are derived from EOG signals of four type eye movement and recorded using the EEG Data Acquisition System Neurofax EEG–9200. The electrodes were attached to the subjects on the forehead and below the eye. The data is acquired from 15 subjects in a quiet room, in which the recorded data is composed by four different eye movements that are upward, downward, towards to left and towards to right. Wavelet scalogram algorithm is used as the tool because of its capable to distribute the EOG signals energy of eye movement with the change of time and frequency. From the results, it indicated that the energy distribution of EOG signals exhibit different patterns in their corresponding movements as follow: level 6 (8–16 Hz) for left eye movement; level 7 (4–8 Hz) for upward; level 8 (2–4 Hz) for right and level 9 (1–2 Hz) for downward. Key words: Electro–oculogram; eye movement; signal potentials; wavelet transform; scalogram

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Experimental Investigation on the Performance of Signal Processing Tools for the Analysis of Mechanical Vibrations in Rotor Rubbing

International Journal of Applied Mechanics ◽

10.1142/s1758825121500186 ◽

2021 ◽

pp. 2150018

Author(s):

Eduardo Rubio ◽

César Chávez-Olivares ◽

Alejandro Cervantes-Herrera

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Hurst Exponent ◽

Signal Analysis ◽

Laboratory Tests ◽

Fourier Transforms ◽

Data Acquisition System ◽

Rotor Speed ◽

Mechanical Vibrations ◽

Run Up

Rubbing is an important problem in machinery industry which occurs when a rotating element hits a stationary part. This rotor-to-stator rub may result in the catastrophic breakdown of the machine. In this work, the phenomenon of rotor rubbing is analyzed from the perspective that the signal analysis tools that are in use today to detect this defect emphasize or highlight particular aspects of the studied phenomenon. So, sometimes it is necessary to use more than one tool to deepen the understanding of the problem. For this purpose, laboratory tests were performed on a rotor system with a rubbing mechanism, while mechanical vibrations were measured with an accelerometer and a data acquisition system. Experiments were carried out for fixed rotor speed, and for run-up and run-down rotor speed conditions. The analysis approach included various processing tools to study their capabilities in rubbing detection: Root Mean Square (RMS), Fourier transform, Wavelet transform and Hurst exponent. Fixed rubbing conditions show similar results for RMS and Hurst exponent on the information obtained. For variable run-up and run-down rotor speed conditions, the Hurst exponent shows predictability, a fact that can be used for rub detection. However, the Wavelet and Fourier Transforms operated in a very distinct way. Although both transforms give frequency information, Fourier transform results in a more detailed frequency analysis, while the Wavelet transform can give time localization of the rubbing phenomenon.

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CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

10.21203/rs.3.rs-27669/v1 ◽

2020 ◽

Author(s):

Diego Fabian Collazos Huertas ◽

Andres Marino Alvarez Meza ◽

German Castellanos Dominguez

Keyword(s):

Wavelet Transform ◽

Motor Imagery ◽

Continuous Wavelet Transform ◽

Brain Activity ◽

Machine Learning Techniques ◽

Continuous Wavelet ◽

Brain Response ◽

Power Spectral ◽

Learning Techniques ◽

Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

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