Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

Abstract

Electroencephalogram (EEG) and its sub-bands represent electrical pattern of human brain. EEG signal contains transient components, spikes, and different types of artifacts due to eye blinking, movement of the person, anxiety, and so forth, during EEG capture. Wavelet transforms are powerful mathematical tool for sampling approximation to get clean EEG. It also helps in filtering, sampling, interpolation, noise reduction, signal approximation and signal enhancement, and feature extraction. In this paper, we have analyzed artifact cleaning via PSD graphs and statistical features extracted from motor imagery EEG-like standard deviation variance. For this, we considered 19 channels EEG signal and applied orthogonal Daubechies wavelet, bi-orthogonal rbio wavelet and Coifman wavelets to check the better performance of different wavelets. Coifman wavelet uses both scaling function and vanishing moments for sampling approximation and hence give smooth sampling compared to rbio and Daubechies wavelet transforms. Coif is a compactly supported wavelet system which also helps in smooth sampling approximations than other wavelets in the state of arts. The detailed coefficients and approximate coefficients can be further used for extracting features from EEG and classification purposes. Artifacts cleaning is thus observed better in coif wavelet analysis compared to other wavelets from the power distributions as power spectral density (PSD) graphs, standard deviation and variance obtained. Matlab R2013b is used for filtering and sampling EEG. Python 2.7 is used for statistical features extraction.

HAPPILEE: The Harvard Automated Processing Pipeline In Low Electrode Electroencephalography, a standardized software for low density EEG and ERP data

“…We selected these family/order combinations as they share shapes similar to those found in EEG signals and they are all orthogonal wavelet functions, which optimizes decomposition and reconstruction of the EEG signal from the wavelet transform (Strang and Nguyen, 1996). Moreover, prior literature indicates these wavelet families and specific orders have performed well on electrophysiological data (Al-Qazzaz et al, 2015; Alyasseri et al, 2017; Harender and Sharma, 2018; Lema-Condo et al, 2017; Nagabushanam et al, 2020). Using these wavelet families, we evaluated whether there was biased data rejection at any of the data frequencies in the clean 30 second segments by evaluating correlations between data pre-waveleting and post-waveleting at specific canonical frequencies.…”

Effect of artifact removal on EEG based motor imagery BCI applications

“…Among orthogonal Daubechies wavelet, bi-orthogonal Rbio wavelet and Coifman wavelets, Coifman wavelets shows preferable results because it is a compactly supported wavelet system which aids in smooth sampling approximation compared to other wavelets. Decomposition filters and reconstruction filters can then be used to gain the clean EEG signal [12]. Study have been made to remove ocular, muscle, blink artifacts using the stationary wavelet transform as well as combinations of wavelet transform and other methods [13], [14], [15].…”

“…The EEG is then segmented into separate epochs (around 1 second time window) of N samples. The epoch, xi duration is crucial for the proposed design for later stages [12].…”

Effect of psychoacoustic annoyance on EEG signals of tractor drivers

“…In this study, each signal is decomposed by DWT to 8 levels using a mother wavelet of Daubechies 8 (db8). The db8 function is widely used for removing artifacts from EEG signal (Nagabushanam et al, 2020;Luo et al, 2016). The 8 level decomposition of EEG signals resulted in one approximation and eight details coefficients.…”