Welch power spectral density of EEG signal generated from dyslexic children

2014 ◽  
Author(s):  
C. W. N. F. Che Wan Fadzal ◽  
W. Mansor ◽  
L. Y. Khuan ◽  
N. B. Mohamad ◽  
Z. Mahmoodin ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Eeg Signal ◽  
Power Spectral

scholarly journals Performance of modified power spectral density features in EEG signal classification

2018 ◽  
Vol 9 (3S) ◽  
pp. 830 ◽  
Author(s):  
F.H.K. Zaman ◽  
N.A.M. Shukur ◽  
N. Hamzah ◽  
N.M. Zaini ◽  
Z.I. Rizman
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Signal Classification ◽  
Eeg Signal ◽  
Power Spectral ◽  
Eeg Signal Classification

CLASSIFICATION OF ICTAL EEG USING MODELING BASED SPECTRAL AND TEMPORAL FEATURES ON INSTANTANEOUS AMPLITUDE-FREQUENCY COMPONENTS OF IMFs

2018 ◽  
Vol 30 (06) ◽  
pp. 1850042 ◽  
Author(s):  
K. S. Biju ◽  
M. G. Jibukumar
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Classification Accuracy ◽  
Spectral Features ◽  
Eeg Signal ◽  
Instantaneous Amplitude ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Temporal Features ◽  
Power Spectral

In the present study, a method for classifying the different ictal stages in electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are indicated by ictal activities, which trigger widespread neurological disorders other than stroke and thus affect the world population. In this work, a novel ictal classification method that combines the spectral and temporal features of twin components in Hilbert–Huang transform is proposed. Spectral features of instantaneous amplitude (IA) function are obtained based on the power spectral density of autoregressive (AR) modeling. Here four different cases of ictal activities of EEG signal are classified. In each case first and second intrinsic mode function of Hilbert–Huang transform are tabulated. The power spectral density of AR(6) and AR(10) model are done for IA1 and IA2 components of each case. Temporal features of either instantaneous frequency (IF) function or IA are computed. The feature vectors are tested in a well-known database of different classes in interictal, ictal, and normal activities of EEG signals. The discriminating power of each vector is evaluated through one-way analysis of variance, and the classification results are verified using an artificial neural network (ANN) classifier. The performance of the classifier was assessed in term of sensitivity, specificity, and total classification accuracy. The spectral features of the AR(10) of IA and the temporal features of IA yielded 100% accuracy, 100% sensitivity, and 100% specificity in the ictal classification. By contrast, these features obtained only 83.33% of the total classification accuracy in ictal and interictal EEG signal.

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 Prognosis of Sleep Bruxism Using Power Spectral Density Approach Applied on EEG Signal of Both EMG1-EMG2 and ECG1-ECG2 Channels

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 82553-82562 ◽  
Author(s):  
Dakun Lai ◽  
Md Belal Bin Heyat ◽  
Faez Iqbal Khan ◽  
Yifei Zhang
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Sleep Bruxism ◽  
Eeg Signal ◽  
Power Spectral

scholarly journals Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation

2019 ◽  
Vol 3 (1) ◽  
pp. 17-25
Author(s):  
Nursuci Putri Husain ◽  
Nurseno Bayu Aji
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Principle Component Analysis ◽  
Visual Analysis ◽  
Component Analysis ◽  
Eeg Signal ◽  
Multi Layer Perceptron ◽  
Principle Component ◽  
Power Spectral ◽  
Welch Method

Electroencephalogram (EEG) signal is a signal that could become an information for study about disorders of brain function  such as Epilepsi. EEG that detected in epileptic seizures produce patterns that allow doctors to distinguish it from normal conditions. However, a visual analysis can not be done continuously. This study proposed a new hybrid method of EEG signal classification using Power Spectral Density (PSD) based on Welch method, Principle Component Analysis (PCA), and Multi Layer Perceptron Backpropagation.There are 3 main stages in this study, firstly preprocessing the dataset of EEG signals by Power Spectral Density (PSD) based on Welch method, then Principle Component Analysis (PCA) as a method of  dimensionallity reduction of the EEG signal data and the Multi Layer Perceptron Backpropagation for classifying a signal. Based on experimental results, the proposed method is successfully obtain high accuracy for the 80-20% training-testing partition (99.68%).  

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