Noise Removal in EEG Signals Using SWT–ICA Combinational Approach

2018 ◽  
pp. 217-224 ◽  
Author(s):  
Apoorva Mishra ◽  
Vikrant Bhateja ◽  
Aparna Gupta ◽  
Ayushi Mishra
Keyword(s):  
Noise Removal ◽  
Eeg Signals

A Study on Discrete Wavelet-Based Noise Removal from EEG Signals

2010 ◽  
pp. 593-599 ◽  
Author(s):  
K. Asaduzzaman ◽  
M. B. I. Reaz ◽  
F. Mohd-Yasin ◽  
K. S. Sim ◽  
M. S. Hussain
Keyword(s):  
Noise Removal ◽  
Discrete Wavelet ◽  
Eeg Signals

scholarly journals Epileptic Seizures Prediction Using Machine Learning Methods

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Syed Muhammad Usman ◽  
Muhammad Usman ◽  
Simon Fong
Keyword(s):  
Machine Learning ◽  
Epileptic Seizures ◽  
True Positive Rate ◽  
Noise Removal ◽  
Machine Learning Techniques ◽  
True Positive ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Positive Rate ◽  
Anticipation Time

Epileptic seizures occur due to disorder in brain functionality which can affect patient’s health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures’ sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects.

scholarly journals Denoising of EEG signal based on word imagination using ICA for artifact and noise removal on unspoken speech

2021 ◽  
Vol 22 (1) ◽  
pp. 83
Author(s):  
Efy Yosrita ◽  
Rosida Nur Aziza ◽  
Rahma Farah Ningrum ◽  
Givary Muhammad
Keyword(s):  
Noise Removal ◽  
Physical Activities ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal Ratio ◽  
Eye Blink ◽  
Eeg Data ◽  
Word Classification ◽  
Electroencephalogram Eeg ◽  
Electrical Activities

<span>The purpose of this research is to observe the effectiveness of independent component analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to signal ratio (ASR) is used to measure the effectiveness of ICA in this study. If the ratio is getting larger, the ICA method is considered effective for clearing noise and artifacts from the EEG data. Based on the experiment, the obtained ASR values from 11 subjects on 14 electrodes amounted are within the range of 0,910 to 1,080. Thus, it can be concluded that ICA is effective for removing artifacts from EEG signals based on word imagination.</span>

Noise Removal from Epileptic EEG signals using Adaptive Filters

2018 ◽  
pp. 37-47
Author(s):  
Rekh Ram Janghel ◽  
Satya Prakash Sahu ◽  
Gautam Tatiparti ◽  
Mangesh Kose
Keyword(s):  
Adaptive Filters ◽  
Noise Removal ◽  
Eeg Signals

scholarly journals Performance Analysis of Adaptive Filters with various Wavelets for Noise Removal in EEG Signals

2019 ◽  
Vol 9 (2) ◽  
pp. 3071-3076
Keyword(s):  
Adaptive Filters ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Performance Parameters ◽  
Eeg Signals ◽  
Analysis Techniques ◽  
Wrong Diagnosis ◽  
Wavelet Transformations

Noise removal from recorded EEG signal is most essential for better analysis of brain disorders. During recoding time, EEG signals are usually contaminated by various noise and distortions due to several artifacts. These noisy EEG signals may lead to wrong diagnosis of brain disorders. There are several techniques available to remove the noise from EEG signals. But these techniques are unable to remove the noise completely. However, they can minimize the noise in EEG signals so that the physicians can predict brain disorders. This work presents to minimize the noise by Discrete Wavelet Transform Methods using haar, db2, symlet and coiflet wavelets. EEG original signals from public EEG database are used for experimentation and wavelet transformations, are applied by using Matlab code. The filters performance is measured and analyzed on the basis of performance parameters like SNR and MSE which are calculated for various step sizes of signal and filter orders. Wavelet analysis techniques shows better performance when compared to others

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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