scholarly journals Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

2019 ◽  
Vol 9 (12) ◽  
pp. 352
Author(s):  
Mohammad Shahbakhti ◽  
Maxime Maugeon ◽  
Matin Beiramvand ◽  
Vaidotas Marozas
Keyword(s):  
Wavelet Transform ◽  
Correlation Coefficient ◽  
Signal To Noise Ratio ◽  
Low Complexity ◽  
Stationary Wavelet Transform ◽  
Eeg Signals ◽  
Real Time Processing ◽  
Eye Blink ◽  
Eye Blinks ◽  
Components Analysis

The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

Image Quality Improvement Using Shift Variant and Shift Invariant Based Wavelet Transform Methods

2017 ◽  
Vol 8 (3) ◽  
pp. 42-54
Author(s):  
Sugandha Agarwal ◽  
O. P. Singh ◽  
Deepak Nagaria ◽  
Anil Kumar Tiwari ◽  
Shikha Singh
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Computation Time ◽  
Performance Comparison ◽  
Local Information ◽  
Discrete Wavelet ◽  
Stationary Wavelet Transform ◽  
Visual Evaluation ◽  
Multi Scale ◽  
Image Quality Improvement

The concept of Multi-Scale Transform (MST) based image de-noising methods is incorporated in this paper. The shortcomings of Fourier transform based methods have been improved using multi-scale transform, which help in providing the local information of non-stationary image at different scales which is indispensable for de-noising. Multi-scale transform based image de-noising methods comprises of Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). Both DWT and SWT techniques are incorporated for the de-noising of standard images. Further, the performance comparison has been noted by using well defined metrics, such as, Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Computation Time (CT). The result shows that SWT technique gives better performance as compared to DWT based de-noising technique in terms of both analytical and visual evaluation.

scholarly journals Detection of Human Brain Activities in a Normal Human being

2019 ◽  
Vol 8 (10) ◽  
pp. 4004-4007
Keyword(s):  
Human Brain ◽  
Motor Imagery ◽  
Crucial Role ◽  
Software Tool ◽  
Eeg Signals ◽  
Eye Blink ◽  
Eye Blinks ◽  
Eeg Data ◽  
Normal Human ◽  
The Brain

Detection of artifacts produced in EEG data by eye blinks is a very common problem in EEG research. In this paper we address the detection of eye blink artifacts in a motor imagery (MI) EEG data. Artifacts are nothing but some kind of disturbances present in the brain signal whose origin is not the brain itself. Detection of unwanted artifacts plays a crucial role to acquire artifact free and clean brain EEG signals to analyze and detect brain activities. There are generally two ways of generation of artifacts. From a recorded signal most common and important artifacts in the form of eye blinks are recognized and encapsulated. In this paper a new software tool named BRAINSTORM is introduced for the detection of eye blink artifacts.

scholarly journals Performance evaluation of a new 3D printed dry-contact electrode for EEG signals measurement

2021 ◽  
Vol 24 (1) ◽  
pp. 287
Author(s):  
Aaisha Diaa-Aldeen Abdullah ◽  
Auns Q. Al-Neami
Keyword(s):  
Correlation Coefficient ◽  
Silver Chloride ◽  
Signal To Noise Ratio ◽  
Skin Impedance ◽  
Impedance Change ◽  
Eeg Signals ◽  
Dry Electrode ◽  
Dry Contact ◽  
3D Printed ◽  
Contact Electrode

Traditional wet silver/silver chloride electrodes are used to record electroencephalography (EEG) signals mainly because of their potential repeatability, excellent signal to noise ratio and biocompatibility. This type of electrode is only suitable for conductive glue, which can irritate the skin and cause injury. In addition, as time goes the conductive gel will be dehydrated so the quality of the EEG signal will decrease. To overcome these problems, 3D printed dry-contact electrodes with multi-pins are designed in this work to measure brain signals without prior preparation or gel application. 3D printed electrodes are made from polylactic acids polymer and coated with suitable materials to enhance the conductivity. Electrode-scalp impedance on human was also measured. To evaluate the dry-contact electrode, EEG measurement are performed in subjects and compared with EEG signals acquired by wet electrode by using linear correlation coefficient. Experimentally results showed that the average electrode-skin impedance change of dry electrode in frontal site (9.42-7.25KΩ) and in occipital site (9.56-8.66KΩ). The correlation coefficient between dry and wet electrodes in frontal site (91.4%) and in occipital site (80%). To conclude, the 3D printed dry-contact electrode can be will promising applied on hairy site and provide a promising solutions for long-term monitoring EEG.

Staring Down Death

Crisis ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 212-217 ◽  
Author(s):  
Thomas E. Joiner ◽  
Melanie A. Hom ◽  
Megan L. Rogers ◽  
Carol Chu ◽  
Ian H. Stanley ◽  
...  
Keyword(s):  
Suicide Risk ◽  
Clinical Utility ◽  
Blink Rate ◽  
Specific Task ◽  
Careful Planning ◽  
Eye Blink ◽  
Eye Blinks ◽  
Potential Clinical Utility ◽  
The Relationship

Abstract. Background: Lowered eye blink rate may be a clinically useful indicator of acute, imminent, and severe suicide risk. Diminished eye blink rates are often seen among individuals engaged in heightened concentration on a specific task that requires careful planning and attention. Indeed, overcoming one’s biological instinct for survival through suicide necessitates premeditation and concentration; thus, a diminished eye blink rate may signal imminent suicidality. Aims: This article aims to spur research and clinical inquiry into the role of eye blinks as an indicator of acute suicide risk. Method: Literature relevant to the potential connection between eye blink rate and suicidality was reviewed and synthesized. Results: Anecdotal, cognitive, neurological, and conceptual support for the relationship between decreased blink rate and suicide risk is outlined. Conclusion: Given that eye blinks are a highly observable behavior, the potential clinical utility of using eye blink rate as a marker of suicide risk is immense. Research is warranted to explore the association between eye blink rate and acute suicide risk.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

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