EEG Signal Waveform Analysis Using Wavelet Decomposition

EEG signal waveform analysis is a commonly used method that helps researchers understand the characteristics and patterns of brain activity. For signal waveform analysis, one prevalent approach involves wavelet decomposition. This mathematical technique decomposes complex signals into components with different frequencies and amplitudes, enabling better understanding of signal features. After decomposition, the waveforms can be plotted using visualization tools to further observe and analyze changes and trends in EEG signals. Through this analytical method, researchers can deeply investigate EEG signal properties and obtain comprehensive information. Implementation typically involves using wavelet transformation functions (such as pywt.wavedec in Python or wavedec in MATLAB) to decompose signals into multiple resolution levels. The plotting process usually utilizes visualization libraries (like matplotlib in Python or plot functions in MATLAB) to display approximation and detail coefficients across different frequency bands. Key parameters include selecting appropriate wavelet families (e.g., Daubechies, Symlets), decomposition levels based on signal sampling rate, and configuring plot parameters for clear multi-subplot visualizations of wavelet coefficients.