Application of stochastic methods, wavelet transformations and support vectors for the study of electroencephalogram signals

This study explores the application of modern data processing methods - wavelet transformation, stochastic methods, and Support Vector Machine (SVM) - on real electroencephalogram (EEG) signals from open databases. Analyzing EEG signals is crucial for medical diagnostics and neuroscience, requiring sophisticated techniques due to high dimensionality and noise. Wavelet transformation allows decomposition of signals into frequency components with varying temporal resolutions, facilitating time-frequency analysis. Stochastic methods utilize probabilistic models for modeling random processes and analyzing data statistics. Meanwhile, SVM is a machine learning algorithm that identifies the optimal hyperplane to separate classes, enhancing generalization, particularly with complex nonlinear data. When comparing these methods, the specific data type and task should be considered: wavelet transformation is ideal for signal processing, stochastic methods are used for random processes, and SVM excels in classification tasks. Thus, selecting the most suitable approach should be based on a comparative analysis of method effectiveness in a particular context. This study will discuss these concepts and present examples of applying these techniques to EEG data, contributing to the analysis and classification of brain activity and the identification of pathologies.