scholarly journals Denoising of EEG Signals using Wavelets and SIMULINK Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 335-339
Keyword(s):  
Electrical Activity ◽  
Signal To Noise Ratio ◽  
Standard Technique ◽  
Statistical Properties ◽  
Eeg Signal ◽  
Frequency Range ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Electroencephalogram Eeg ◽  
Ocular Artifacts

The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task due to the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using both matlab coding as well as SIMULINK DWT and IDWT blocks by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks is explained in detail in the paper under the methodology heading. In this paper the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it.

scholarly journals Denoising of EEG Signal using Matlab and SIMULINK Techniques and Estimation of Power Spectral Density of EEG Signal using SIMULINK AR Models

2019 ◽  
Vol 9 (2) ◽  
pp. 418-422
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Signal To Noise Ratio ◽  
Standard Technique ◽  
Research Paper ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Power Spectral ◽  
Electroencephalogram Eeg ◽  
Ocular Artifacts

The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task by reason of the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using wavelets (matlab coding) as well as using SIMULINK DWT and IDWT blocks and estimated the SNR. In the next stage the output of IDWT block was taken as input to Burg model and Yule walker model to estimate the power spectral density of EEG signal by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks and estimation of power spectral density of denoised EEG signal using Burg model and Yule walker model was explained in detail in the paper under the methodology heading. In this research paper, the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it and estimated the power spectral density of denoised EEG signal obtained from dB4 wavelet as it is providing better SNR than other wavelets mentioned in the results.

scholarly journals Performance Analysis of IIR & FIR Windowing Techniques in Electroencephalography Signal Processing

2019 ◽  
Vol 8 (10) ◽  
pp. 3568-3578
Keyword(s):  
Performance Analysis ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Data Retrieval ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Noise Ratio

A very small amplitude (μV) of the electroencephalography (EEG) signal is infected by diverse artifacts. These artifacts have an effect on the distinctiveness of the signal because of which medical psychoanalysis and data retrieval is difficult. Therefore, EEG signals are initially preprocessed to eliminate the artifacts to produce signals that can serve as a base for further processing and analysis. Different filters are implemented to eliminate the artifacts present in the EEG signal. Recent research shows that window technique Finite Impulse Response (FIR) filter is usually used. In this paper, digital Infinite Impulse Response (IIR) filter and different Finite Impulse Response (FIR) window filters (Hanning, Hamming, Kaiser, Blackman) of various orders are implemented to eradicate the random noise added to EEG signals. Their performance analysis has been done in Matlab (R2016a) by calculating the mean square error, mean absolute error, signal to noise ratio, peak signal to noise ratio and cross-correlation. The results show that Kaiser Window based finite impulse response filter outperforms in removing the noise from the electroencephalogram signal. This research focuses on eradicating random noise in electroencephalogram signals but this approach will be extended to a different source of electroencephalogram contamination.

scholarly journals Estimation of Number of Levels of Scaling the Principal Components in Denoising EEG Signals

2021 ◽  
Vol 14 (01) ◽  
pp. 425-433
Author(s):  
B. Krishna Kumar
Keyword(s):  
Principal Component Analysis ◽  
Standard Method ◽  
Principal Components ◽  
Principal Component ◽  
Data Sets ◽  
Eeg Signal ◽  
Eeg Signals ◽  
High Quality ◽  
Electroencephalogram Eeg ◽  
Ocular Artifacts

Electroencephalogram (EEG) is basically a standard method for investigating the brain’s electrical action in diverse psychological and pathological states. Investigation of Electroencephalogram (EEG) signal is a tough task due to the occurrence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. By and large EEG signals falls in the range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex[1]. In this research paper, Principal Component Analysis is employed in denoising the EEG signals. This paper explains up to what level the scaling of principal components have to be done. This paper explains the number of levels of scaling the principal components to get the high quality EEG signal. The work has been carried out on different data sets and later estimated the SNR.

scholarly journals Removing Artifacts From Eeg Signal Using Wavelet Transform and Conventional Filters

2021 ◽  
Vol 15 ◽  
pp. 172-177
Author(s):  
Meryem Felja ◽  
Asmae Bencheqroune ◽  
Mohammed Karim ◽  
Ghita Bennis
Keyword(s):  
Wavelet Transform ◽  
Eye Movements ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Eeg Signal ◽  
Mean Square ◽  
Signal To Noise ◽  
Common Criteria ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is a signal of an electrical nature reflecting the neuronal activities of the brain. It is used for the diagnosis of certain cerebral pathologies. However, it becomes more difficult to identify and analyze it when it is corrupted by artifacts of non-cerebral origin such as eye movements, cardiac activities ..., therefore, it is essential to remove these parasitic signals. In literature, there are different techniques for removing artifacts. This paper proposes and discusses a new EEG de-noising technique, based on a combination of wavelet transforms and conventional filters. The results of the proposed method are evaluated using three common criteria: signal-to-noise-ratio (SNR), mean square error (MSE) and cross correletion function (CCF). These experimental results demonstrate that the proposed approach can be an effective tool for removing artifact without suppression of any signal components.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

scholarly journals Artefact Removal from EEG Signals using Total Variation De-noising

2020 ◽  
Vol 9 (5) ◽  
pp. 2357-2361
Keyword(s):  
Root Mean Square Error ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Health Issues ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Medical Practitioners ◽  
Artefact Removal ◽  
Two Parameters

Artefacts removing (de-noising) from EEG signals has been an important aspect for medical practitioners for diagnosis of health issues related to brain. Several methods have been used in last few decades. Wavelet and total variation based de-noising have attracted the attention of engineers and scientists due to their de-noising efficiency. In this article, EEG signals have been de-noised using total variation based method and results obtained have been compared with the results obtained from the celebrated wavelet based methods . The performance of methods is measured using two parameters: signal-to-noise ratio and root mean square error. It has been observed that total variation based de-noising methods produce better results than the wavelet based methods.

scholarly journals Maximum Signal Fraction Analysis for Enhancing Signal-to-Noise Ratio of EEG Signals in SSVEP-Based BCIs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 85452-85461 ◽  
Author(s):  
Qingguo Wei ◽  
Shan Zhu ◽  
Yijun Wang ◽  
Xiaorong Gao ◽  
Hai Guo ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Maximum Signal

P300 Feature Extraction of Visual and Auditory Evoked EEG Signal

2014 ◽  
Vol 490-491 ◽  
pp. 1374-1377 ◽  
Author(s):  
Xiao Yan Qiao ◽  
Jia Hui Peng
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Evoked Potentials ◽  
Brain Computer Interface ◽  
Auditory Stimulation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Significant Issue ◽  
Eeg Signals ◽  
Electroencephalogram Eeg

It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

scholarly journals Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiongliang Xiao ◽  
Yuee Fang
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Signal To Noise Ratio ◽  
Large Individual ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Time Frequency ◽  
Morlet Wavelet Transform

Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability.

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