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 Removal of ocular artifacts from EEG signals using adaptive threshold PCA and Wavelet transforms

2011 ◽  
pp. 42-46
Author(s):  
P. Ashok Babu ◽  
K. V.S.V.R. Prasad
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Adaptive Threshold ◽  
Eeg Signals ◽  
Threshold Method ◽  
Eye Blinks ◽  
Elapsed Time ◽  
Electroencephalogram Eeg ◽  
Ocular Artifacts

It becomes more difficult to identify and analyze the Electroencephalogram (EEG) signals when it is corrupted by eye movements and eye blinks. This paper gives the different methods how to remove the artifacts in EEG signals. In this paper we proposed wavelet based threshold method and Principal Component Analysis (PCA) based adaptive threshold method to remove the ocular artifacts. Compared to the wavelet threshold method PCA based adaptive threshold method will gives the better PSNR value and it will decreases the elapsed time.

Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration

2017 ◽  
Vol 16 (3) ◽  
Author(s):  
Shofiqul Islam ◽  
Sonia Anand ◽  
Jemila Hamid ◽  
Lehana Thabane ◽  
Joseph Beyene
Keyword(s):  
Principal Component Analysis ◽  
Data Integration ◽  
Principal Components ◽  
Mirna Expression ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Data Sets ◽  
Data Set ◽  
Multiple Data Sets

AbstractLinear principal component analysis (PCA) is a widely used approach to reduce the dimension of gene or miRNA expression data sets. This method relies on the linearity assumption, which often fails to capture the patterns and relationships inherent in the data. Thus, a nonlinear approach such as kernel PCA might be optimal. We develop a copula-based simulation algorithm that takes into account the degree of dependence and nonlinearity observed in these data sets. Using this algorithm, we conduct an extensive simulation to compare the performance of linear and kernel principal component analysis methods towards data integration and death classification. We also compare these methods using a real data set with gene and miRNA expression of lung cancer patients. First few kernel principal components show poor performance compared to the linear principal components in this occasion. Reducing dimensions using linear PCA and a logistic regression model for classification seems to be adequate for this purpose. Integrating information from multiple data sets using either of these two approaches leads to an improved classification accuracy for the outcome.

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 A New Signal Processing Approach for Discrimination of EEG Recordings

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 155-168 ◽  
Author(s):  
Hossein Hassani ◽  
Mohammad Yeganegi ◽  
Emmanuel Silva
Keyword(s):  
Principal Component Analysis ◽  
Brain Activity ◽  
Singular Spectrum Analysis ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Eeg Recordings ◽  
Improving Accuracy ◽  
Electroencephalogram Eeg

Classifying brain activities based on electroencephalogram (EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper, we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are xcompared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further.

scholarly journals A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis

2017 ◽  
Vol 33 (1) ◽  
pp. 15-41 ◽  
Author(s):  
Aida Calviño
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Synthetic Data ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Simple Method ◽  
Processing Times ◽  
The Mean ◽  
Mean Vector

Abstract In this article we propose a simple and versatile method for limiting disclosure in continuous microdata based on Principal Component Analysis (PCA). Instead of perturbing the original variables, we propose to alter the principal components, as they contain the same information but are uncorrelated, which permits working on each component separately, reducing processing times. The number and weight of the perturbed components determine the level of protection and distortion of the masked data. The method provides preservation of the mean vector and the variance-covariance matrix. Furthermore, depending on the technique chosen to perturb the principal components, the proposed method can provide masked, hybrid or fully synthetic data sets. Some examples of application and comparison with other methods previously proposed in the literature (in terms of disclosure risk and data utility) are also included.

scholarly journals Enhanced Epileptic Seizure Detection using Imbalanced Classification

2020 ◽  
Vol 9 (1) ◽  
pp. 2412-2420
Keyword(s):  
Visual Analytics ◽  
Principal Component ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Imbalanced Classification ◽  
Machine Learning Classifiers ◽  
Epileptic Patients ◽  
Eeg Recordings ◽  
Artificial Neural Network Ann ◽  
Electroencephalogram Eeg

Epilepsy is the second most persistent neurological condition, endangering the lives of patients. Though there have been many advancements in neurological imaging approaches, the Electroencephalogram (EEG) still remains to be the most effective tool for testing and diagnosing epileptic patients. The visual analytics of EEG signals is a very prolonged process and always open to the subjective judgment of the physicians. The main goal of our study is to build an automatic classifier that can analyze and detect epilepsy from EEG recordings obtained from epileptic and healthy patients, thus helping the neurosurgeons to diagnose epilepsy in a better way. Synthetic minority oversampling technique (SMOTE) has been used for balancing the EEG dataset and the Principal component analysis (PCA) technique is applied further, for reducing the EEG signal dimensionality. For data classification, seven machine learning classifiers have been used and after comparing the results the authors conclude that Artificial Neural Network (ANN), outperforms the other classifiers by providing an accuracy of 97.82%.

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.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Evaluation functions for integral mapping

2017 ◽  
Vol 921 (3) ◽  
pp. 24-29 ◽  
Author(s):  
S.I. Lesnykh ◽  
A.K. Cherkashin
Keyword(s):  
Principal Component Analysis ◽  
Environmental Factors ◽  
Principal Components ◽  
Principal Component ◽  
Evaluation Function ◽  
Final Value ◽  
Evaluation Functions ◽  
Geographical Environment ◽  
Environmental Background ◽  
Integral Mapping

The proposed procedure of integral mapping is based on calculation of evaluation functions on the integral indicators (II) taking into account the feature of the local geographical environment, when geosystems in the same states in the different environs have various estimates. Calculation of II is realized with application of a Principal Component Analysis for processing of the forest database, allowing to consider in II the weight of each indicator (attribute). The final value of II is equal to a difference of the first (condition of geosystem) and the second (condition of environmental background) principal components. The evaluation functions are calculated on this value for various problems of integral mapping. The environmental factors of variability is excluded from final value of II, therefore there is an opportunity to find the invariant evaluation function and to determine coefficients of this function. Concepts and functions of the theory of reliability for making the evaluation maps of the hazard of functioning and stability of geosystems are used.

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.

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