Serial identification of EEG patterns using adaptive wavelet-based analysis

Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

“…Along with these methods, there are also other approaches used for quantitative classification and feature extraction of the EEG patterns, such as e.g., discriminant analysis and independent component analysis (Makeig et al, 1996 ; Ungureanu et al, 2004 ; Hobson and Hillebrand, 2006 ). However, the wavelet-based methods give better results for classification and allocation of EEG patterns (Sitnikova et al, 2009 , 2014 ; Nazimov et al, 2013 ) than other methods.…”

“…Among various approaches proposed for the classification of oscillatory patterns observed in the EEG recordings (Garrett et al, 2003 ; Dias et al, 2007 ; Siuly et al, 2016 ), some are worth mentioning such as discriminant analysis methods (which were very popular in the 1960s) (Niedermeyer and Lopes da Silva, 2005 ; Hasan et al, 2015 ), independent component analysis (Makeig et al, 1996 ; Ungureanu et al, 2004 ; Hobson and Hillebrand, 2006 ) (often used for finding and eliminating biased artifacts in EEG signals; Jung et al, 2000 ), short-time Fourier transform (Gotman et al, 1973 ), and wavelet-based methods (Hramov et al, 2015 ), including techniques of adaptive mother wavelets (Sitnikova et al, 2009 ; Nazimov et al, 2013 ) and methods based on estimation of event-related synchronization/desynchronization (Morash et al, 2008 ). Nowadays, another classification technique known as artificial neural network (ANN) (Bishop, 2006 ; Haykin, 2008 ) is widely used in computer science, biophysics, deep learning, econometrics, etc.…”

Possibility for Recognition of Psychic Brain Activity with Continuous Wavelet Analysis of EEG

“…[6]. CWT is known to be effective in time-frequency analysis of nonstationary signals (including EEG) and as basis for development of algorithms for automatic detection of specific EEG patterns[7] [8][9].…”