EEG Signal Processing: Wavelet-Based Spectral and Power Spectrum Analysis

EEG signals can be analyzed through computation and visualization of spectrum and power spectrum. This process primarily employs wavelet transform techniques, where the signal decomposition is achieved through multi-level wavelet decomposition algorithms (e.g., using 'wavedec' function in MATLAB/Python). Wavelet coefficient reconstruction involves selectively reconstructing specific frequency bands using 'wrcoef' functions to isolate delta, theta, alpha, beta, and gamma rhythms. The power spectrum calculation for each band utilizes periodogram or Welch's method implementation, with key parameters including window size (e.g., Hann window) and overlap percentage for spectral estimation. These methods enable comprehensive characterization of EEG signal features and dynamics. Further analysis can investigate cross-frequency coupling and functional connectivity between different bands using coherence or phase-amplitude coupling algorithms, advancing the understanding of EEG signal nature and applications in neurological research.