EEG Signal Decomposition into 5 Sub-bands Using Wavelet Transform

This study focuses on decomposing EEG signals into five distinct sub-bands using wavelet transform methodology. The implementation typically involves applying multi-level wavelet decomposition to extract specific frequency components corresponding to standard EEG bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz). Key implementation aspects include selecting appropriate wavelet functions (such as Daubechies or Coiflet wavelets) and determining optimal decomposition levels based on sampling frequency requirements. The algorithm processes raw EEG data through successive high-pass and low-pass filtering operations, followed by downsampling to achieve the desired frequency resolution. This analytical approach enables detailed examination of distinct frequency components within EEG data, facilitating deeper investigation into underlying neural activity patterns. The decomposition process reveals characteristic patterns and unique features exhibited by each sub-band, significantly advancing our understanding of EEG signal processing techniques and their applications in neurological research. Code implementation typically involves using wavelet toolbox functions like wavedec for multi-level decomposition and wrcoef for reconstruction of specific sub-bands, allowing researchers to isolate and analyze individual frequency components with precision.