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.

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 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 Performance Analysis of Classifiers in Identifying NREM Sleep and Awaken Stages from EEG Signals

2020 ◽  
Vol 8 (6) ◽  
pp. 4321-4326
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression ◽  
Linear Regression ◽  
Expectation Maximization ◽  
Principal Component ◽  
Nrem Sleep ◽  
Component Analysis ◽  
Medical Procedure ◽  
Eeg Signals ◽  
Non Linear

Electroencephalogram is a medical procedure which helps in analyzing the activities of the brain through electrical signals. In this paper a simple classification technique of EEG signal into two stages as NREM sleep and awaken stages had been undertaken. Classifying these stages helps the physician to understand the patient's sleep disorder by knowing whether the person's brain is in NREM sleep or awaken stages. Physionet EEG signals are samples of 256 signals per second for 10 seconds duration is used in this work. Then the EEG samples properties are analyzed through various parameters like statistical features, entropy Pearson correlation coefficient, Power spectral density, scatter plots and Hilbert transform plots. The classification of NREM sleep and awaken stage is performed by the ten different classifiers broadly grouped into non linear and hybrid one. The classifiers used include Linear Regression, Non Linear Regression, Logistic Regression, Principal Component Analysis, Kernel Principal Component Analysis, Expectation Maximization, Compensatory Expectation Maximization, Expectation Maximization with Logistic Regression Compensatory Expectation Maximization with Logistic Regression, and Firefly. The performances of the classifiers are analyzed using regular parameters like sensitivity, accuracy, specificity, performance index. The highest accuracy of 95.575% is achieved with linear regression for awaken signal and an accuracy of 95.315% is achieved using kernel PCA for sleep signal.

EEG Alpha and Gamma Modulators Mediate Motion Sickness-Related Spectral Responses

2016 ◽  
Vol 26 (02) ◽  
pp. 1650007 ◽  
Author(s):  
Shang-Wen Chuang ◽  
Chun-Hsiang Chuang ◽  
Yi-Hsin Yu ◽  
Jung-Tai King ◽  
Chin-Teng Lin
Keyword(s):  
Motion Sickness ◽  
Principal Component ◽  
Component Analysis ◽  
Spectral Change ◽  
Point Scale ◽  
Eeg Signals ◽  
Independent Components ◽  
Eeg Alpha ◽  
Electroencephalogram Eeg ◽  
Spectral Responses

Motion sickness (MS) is a common experience of travelers. To provide insights into brain dynamics associated with MS, this study recruited 19 subjects to participate in an electroencephalogram (EEG) experiment in a virtual-reality driving environment. When riding on consecutive winding roads, subjects experienced postural instability and sensory conflict between visual and vestibular stimuli. Meanwhile, subjects rated their level of MS on a six-point scale. Independent component analysis (ICA) was used to separate the filtered EEG signals into maximally temporally independent components (ICs). Then, reduced logarithmic spectra of ICs of interest, using principal component analysis, were decomposed by ICA again to find spectrally fixed and temporally independent modulators (IMs). Results demonstrated that a higher degree of MS accompanied increased activation of alpha ([Formula: see text]) and gamma ([Formula: see text]) IMs across remote-independent brain processes, covering motor, parietal and occipital areas. This co-modulatory spectral change in alpha and gamma bands revealed the neurophysiological demand to regulate conflicts among multi-modal sensory systems during MS.

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