The EEG signal, while providing valuable insights into brain activity through amplitude and frequency data, is often accompanied by undesirable components. These include various artifacts and noise that can obscure the underlying neural information. Effectively separating these unwanted elements from the genuine brain signals is crucial for accurate interpretation and analysis in neuroscience and clinical applications. Advanced signal processing techniques are typically employed to enhance the signal-to-noise ratio, allowing researchers and clinicians to extract meaningful data from the complex electrical patterns recorded by the EEG.