… and EEG, it is still very new. This is our motivation to introduce WST to EEG signals for feature
extraction and also to do a comparative study with the most popular wavelet methods like …

… diagnosis approach using signal processing techniques and … feature outperformed all
the other feature extraction techniques. The interesting fact is noted that the analysis of EEG …

… We consider two different feature extraction methods in this … machine learning classifier for
EEG signal analysis [15]. Linear … wavelet transformation and various statistical features are …

… Other features extraction based approaches decomposed multi-channel EEG data … As
a consequence, if there is any mutual information in multi-channel data then that will be …

… MATLAB 2015b software was used for performing the task of noise removal, signal
segmentation and feature extraction and the rest of the works were performed in Python 3.8.8. …

… been used for feature extraction, perform feature selection and … the human brain in
data processing and generating patterns … Wavelet transform is a tool that provides a two-dimensional …

… classification process of EEG signals collected from healthy and schizophrenic individuals
… TQWT is one of the new generation wavelet transforms (WT) frequently used in the literature […

… feature extraction methods to classify the two mentioned groups. In this research, it is shown
that the selection of EEG … discrete wavelet transform (DWT) is a versatile signal processing …

… signal processing methods and extracted features. Generally, a CAD system based on the
signal processing and feature extraction … the wavelet transform is widely used to analyze EEG …

… domains via Fourier Transform (FT) [67] or Wavelet Transform (WT) [68], respectively. … of
EEG signal processing for ML. This upward trend is expected to continue as signal processing …