Automated technique for EEG signal processing to detect seizure with optimized Variable Gaussian Filter and Fuzzy RBFELM classifier

2022 ◽  
Vol 74 ◽  
pp. 103450
Author(s):  
A Harishvijey ◽  
J Benadict Raja
Keyword(s):  
Signal Processing ◽  
Eeg Signal ◽  
Gaussian Filter ◽  
Automated Technique ◽  
Eeg Signal Processing

scholarly journals EEG Signal Processing for Epilepsy

10.5772/31609 ◽  
2012 ◽  
Author(s):  
Carlos Guerrero-Mosquera ◽  
Armando Malanda ◽  
Angel Navia-Vazquez
Keyword(s):  
Signal Processing ◽  
Eeg Signal ◽  
Eeg Signal Processing

ARTIFACT REMOVAL AND BRAIN RHYTHM DECOMPOSITION FOR EEG SIGNAL USING WAVELET APPROACH

2016 ◽  
Vol 78 (7-5) ◽  
Author(s):  
Syarifah Noor Syakiylla Sayed Daud ◽  
Rubita Sudirman
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Biomedical Signal ◽  
Brain Rhythm ◽  
Time And Energy ◽  
Eeg Signal Processing

This recent study introduces and discusses briefly the use of wavelet approach in removing the artifacts and extraction of features for electroencephalography (EEG) signal. Many of new approaches have been discovered by the researcher for processing the EEG signal. Generally, the EEG signal processing can be divided into pre-processing and post-processing.  The aim of processing is to remove the unwanted signal and to extract important features from the signal.  However, the selections of non-suitable approach affect the actual result and wasting the time and energy.  Wavelet is among the effective approach that can be used for processing the biomedical signal.  The wavelet approach can be performed in MATLAB toolbox or by coding, that require a simple and basic command. In this paper, the application of wavelet approach for EEG signal processing is introduced. Moreover, this paper also discusses the effect of using db3 mother wavelet with 5th decomposition level of stationary wavelet transform and db4 mother wavelet with 7th decomposition level of discrete wavelet transform in removing the noise and decomposing of the brain rhythm. Besides, the simulation result are also provided for better configuration.

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